Apparatus for determining association variables

ABSTRACT

An apparatus, and related method, for determining one or more association variables is described. The apparatus includes at least one processor, at least one memory, and at least one program module. The program module is stored in the memory and is configurable to be executed by the processor. The program module includes instructions for determining a statistical relationship between one or more temporal onsets corresponding to one or more events and a pattern of occurrence of a compound variable. The compound variable corresponds at least to a pattern of occurrence of a first variable and a pattern of occurrence of a second variable. The determining includes contributions from presence and absence information in the pattern of occurrence of the compound variable.

CROSS REFERENCE TO RELATED APPLICATIONS

This application claims priority under 35 U.S.C. 120 as a DivisionalPatent Application of U.S. patent application Ser. No. 11/177,063,“Apparatus for Determining Association Variables,” filed on Jul. 8, 2005(now U.S. Pat. No. 7,223,234), which claims priority under 35 U.S.C.119(e) to U.S. Provisional Application Ser. No. 60/601,480, “MedicalInformatics System,” filed on Aug. 14, 2004, to U.S. ProvisionalApplication Ser. No. 60/591,300, entitled “Healthcare Provider-PatientInteraction Management System,” filed on Jul. 27, 2004, and to U.S.Provisional Application Ser. No. 60/587,003, entitled “MedicalInformatics System,” filed on Jul. 10, 2004, the contents of each ofwhich are herein incorporated by reference.

This application is also related to U.S. patent application Ser. No.11/178,044, “Apparatus for Collecting Information,” filed on Jul. 8,2005, to U.S. patent application Ser. No. 11/496,755, “Apparatus forDetermining Association Variables,” filed on Jul. 31, 2006, to U.S.patent application Ser. No. 11/529,054, “Apparatus for DeterminingAssociation Variables,” filed on Sep. 27, 2006, to U.S. patentapplication Ser. No. 11/704,735, “Apparatus for Providing InformationBased on Association Variables,” filed on Feb. 9, 2007, and to U.S.patent application Ser. No. 11/809,807, “Apparatus for AggregatingIndividuals Based on Association Variables,” filed on May 31, 2007.

FIELD OF THE INVENTION

The present invention relates generally to an apparatus, and relatedmethods, for processing data, and more specifically, for determiningstatistical relationships.

BACKGROUND OF THE INVENTION

Statistical learning problems may be categorized as supervised orunsupervised. In supervised learning, the goal is to predict an outputbased on a number of input factors or variables (henceforth, referred toas variables), where a prediction rule is learned from a set of examples(referred to as training examples) each showing the output for arespective combination of variables. In unsupervised learning, the goalis to describe associations and patterns among a set of variableswithout the guidance of a specific output. An output may be predictedafter the associations and patterns have been determined. Thesecategories are illustrated in FIGS. 1A and 1B, which show data points asa function of weight 110 and height 112. In unsupervised learning 100 inFIG. 1A, the data may be described by input variables weight 110 andheight 112 without any additional information (e.g., labels) that couldhelp to find patterns in the data. Patterns in the data may be found bylearning that there are two distinguished “clusters” of data points(represented by circles or decision boundaries 114 around them). Withineach cluster, data in group A 116 or group B 118 are highly similar(i.e., close) and between clusters data are highly dissimilar (i.e.,further away). When a new data point, i.e., combination of the inputvariables becomes available, it may be categorized as similar to andthus a potential member of one of the clusters that have beendiscovered, or as an outlier or as a member of a new cluster.

In supervised learning 130 in FIG. 1B, additional information about thedata is available. The data points are labeled as Dutch 132 (whitecircles) or American 134 (filled-black circles). This extra informationis exactly the output one wants to predict for future data. Having itavailable for the training data or examples allows predictive decisionboundary 136 to be determined. In general, statistical learning involvesfinding a statistical model that explains the observed data that may beused to analyze new data, e.g., learning a weighted combination ofnumerical variables from labeled training data to predict a class orclassification for a new combination of variables. Determining a modelto predict quantitative outputs (continuous variables) is often referredto as regression. Determining a model to predict qualitative data(discrete categories, such as ‘yes’ or ‘no’) is often referred to asclassification.

Developing models for statistical learning problems involvinglongitudinal data, in which a time series of observations are collectedover a period of time, poses several challenges, including thoseassociated with collecting the data efficiently and accurately. Analysisof the data may also be problematic, in particular, for a class ofproblems where variables associated with time-varying phenomena thathave discrete events or epochs, each epoch having a characteristic onsettime (henceforth referred to as a temporal onset), are sought. Forexample, in such problems there may be limited data and a plurality ofpotential variables to be screened. The analysis, therefore, may beunderdetermined. In addition, the potential variables may not beindependent from one another and/or samples of the potential variablesmay not have a corresponding probability distribution (for example, anormal distribution).

There is a need, therefore, for an analysis technique to address thechallenges described above and to determine variables associated withtime-varying phenomena having discrete epochs.

SUMMARY OF THE INVENTION

An apparatus, and related method, for determining one or moreassociation variables is described. The apparatus may include at leastone processor, at least one memory, and at least one program module. Theprogram module may be stored in the memory and may be configurable to beexecuted by the processor. The program module may include instructionsfor determining a first statistical relationship between one or moretemporal onsets corresponding to one or more events and a pattern ofoccurrence of a compound variable. The compound variable may correspondat least to a pattern of occurrence of a first variable and a pattern ofoccurrence of a second variable. The determining may includecontributions from presence and absence information in the pattern ofoccurrence of the compound variable.

In some embodiments, the compound variable may correspond at least to apattern of occurrence of a first variable during a first set of timeintervals and a pattern of occurrence of a second variable during asecond set of time intervals. Each time interval in a respective set oftime intervals may precede a respective temporal onset in the one ormore temporal onsets.

The program module may include instructions for identifying the firstvariable and the second variable as the association variables inaccordance with the first statistical relationship. The program modulemay include instructions for receiving information including the one ormore temporal onsets corresponding to one or more events and the patternof occurrence of the compound variable. The program module may includeinstructions for providing recommendations to one or more individuals inaccordance with the first variable and the second variable.

The program module may include instructions for generating a pluralityof compound variables and for determining one or more statisticalrelationships for the plurality of compound variables. A respectivecompound variable in the plurality of compound variables may correspondto patterns of occurrence of at least two variables in a set ofvariables, one of at least the two variables occurring during one set oftime intervals and another of at least the two variables occurringduring another set of time interval. Each time interval in a respectiveset of time intervals may precede a respective temporal onset in the oneor more temporal onsets. The program module may include instructions forranking the plurality of compound variables in accordance with the oneor more statistical relationships. The program module may includeinstructions for ranking variables in the set of variables in accordancewith a number of occurrences of the variables in the compound variableshaving respective statistical relationships that approximately exceed astatistical confidence threshold. The statistical confidence thresholdmay be selected such that at least a subset of the ranking isapproximately stable.

The determining may use a non-parametric statistical analysis technique,including a chi-square analysis technique, a log-likelihood ratioanalysis technique and/or a Fisher's exact probability analysistechnique. The determining may use a supervised learning technique,including a support vector machines (SVM) analysis technique and/or aclassification and regression tree (CART) analysis technique.

The pattern of occurrence of the first variable and the pattern ofoccurrence of the second variable may comprise categorical data. Arespective entry in the pattern of occurrence of the compound variablemay be determined by performing a logical operation on correspondingentries in the pattern of occurrence of the first variable and thepattern of occurrence of the second variable. In some embodiments, thelogical operation is a Boolean operation, including AND, OR, NOT and/orXOR.

Time intervals in the first set of time intervals and/or in the secondset of time intervals may be offset in time from the one or moretemporal onsets. Time intervals in the first set of time intervals maybe different than time intervals in the second set of time intervals.The pattern of occurrence of the first variable and the pattern ofoccurrence of the second variable may include presence and absenceinformation. A respective entry in a pattern of occurrence of arespective variable may be considered present if the respective entryapproximately exceeds at least one threshold. In some embodiments, theone or more statistical relationships may at least in part be determinedusing a filter, such as an analog filter and/or a digital filter.

In some embodiments, the association variables are migraine triggers andthe one or more events correspond to one or more migraines experiencedby at least one individual. One or more of the migraine triggers may atleast in part induce a migraine in at least the one individual if atleast the one individual is exposed to one or more of the migrainetriggers.

In some embodiments, the one or more events correspond to an episodicincrease in a severity of one or more symptoms associated with a diseaseand the association variables may trigger the one or more events. Thedisease may include a form of arthritis, rheumatoid arthritis, jointdisease, an auto-immune disorder, an immune-related disorder, aninflammatory disease, lupus, thyroid disease, gout, diabetes, chronicfatigue syndrome, insomnia, depression, a psychological disease,gastrointestinal disease, colitis, ulcerative colitis, inflammatorybowel disease, Crohn's disease, candida, celiac disease, irritable bowelsyndrome, one or more food allergies, one or more food sensitivities,morning sickness, menstrual cramps, chronic pain, back pain, facialpain, fibromyalgia, asthma, migraines, abdominal migraines, cyclicvomiting syndrome, cluster headaches, chronic headaches, tensionheadaches, another type of headache, seizures, epilepsy,neurodermatitis, acne, psoriasis, adiposity, hypertonia, heart disease,hypertension, cardiovascular disease, arteriosclerosis, a form of cancerand/or acquired immune deficiency syndrome.

In another embodiment, an apparatus, and related method, for determiningone or more association variables is described. The apparatus mayinclude at least one processor, at least one memory, and at least oneprogram module. The program module may be stored in the memory and maybe configurable to be executed by the processor. The program module mayinclude instructions for determining a second statistical relationshipbetween a first subset of temporal onsets in a set of temporal onsetsand a pattern of occurrence of at least a third variable during at leasta third set of time intervals. Each time interval in the third set oftime intervals may precede a respective temporal onset in the firstsubset of temporal onsets. The program module may also includeinstructions for identifying at least the third variable as the one ormore association variables in accordance with the second statisticalrelationship. The first subset of temporal onsets may include one ormore onsets corresponding to one or more migraines experienced by atleast the one individual. The set of temporal onsets may include thefirst subset of temporal onsets and one or more temporal onsetscorresponding to one or more additional headaches experienced by atleast the one individual.

The one or more additional headaches may include one or more reboundmigraines, one or more recurrence migraines and/or one or more tensionheadaches. The determining may include contributions from presence andabsence information in the pattern or occurrence of the third variable.

The pattern of occurrence of the third variable may include one or moreentries corresponding to at least a fourth time interval after at leasta respective temporal onset in the first subset of temporal onsets. Arespective migraine corresponding to at least the respective temporalonset may have a duration including at least the fourth time interval.The one or more entries may be excluded when the second statisticalrelationship is determined.

In another embodiment, an apparatus, and related method, for determiningmigraine triggers is described. The apparatus may include at least oneprocessor, at least one memory, and at least one program module. Theprogram module may be stored in the memory and may be configurable to beexecuted by the processor. The program module may include instructionsfor determining a second subset of the migraine triggers for at leastthe one individual. The program module may also include instructions forassociating at least the one individual with one or more groups ofmigraine triggers in accordance with the determined second subset ofmigraine triggers.

In another embodiment, a process for determining one or more associationvariables is described. A first data stream, including one or moretemporal onsets corresponding to one or more events, the pattern ofoccurrence of the first variable and the pattern of occurrence of thesecond variable, may be transmitted. A second data stream, includinginformation that identifies the first variable and the second variablesas the association variables, may be received. The information may bedetermined in accordance with the first statistical relationship betweenthe one or more temporal onsets and the pattern of occurrence of thecompound variable. The compound variable may correspond at least to thepattern of occurrence of the first variable and the pattern ofoccurrence of the second variable. The first statistical relationshipmay include contributions from presence and absence information in thepattern of occurrence of the compound variable.

The disclosed embodiments reduce or eliminate the problems describedabove and provide an analysis technique to determine associationvariables associated with time-varying phenomena having discrete epochs.

BRIEF DESCRIPTION OF THE DRAWINGS

Additional objects and features of the invention will be more readilyapparent from the following detailed description and appended claimswhen taken in conjunction with the drawings.

FIG. 1A is a block diagram illustrating an existing unsupervisedlearning technique.

FIG. 1B is a block diagram illustrating an existing supervised learningtechnique.

FIG. 2 is a block diagram illustrating an embodiment of a system forcollecting and analyzing data, and for providing recommendations.

FIG. 3 is a block diagram illustrating an embodiment of a server orcomputer.

FIG. 4 is a block diagram illustrating an embodiment of a computer.

FIG. 5 is a block diagram illustrating an embodiment of a device.

FIG. 6A is a block diagram illustrating an embodiment of aquestionnaire.

FIG. 6B is a block diagram illustrating an embodiment of aquestionnaire.

FIG. 6C is a block diagram illustrating an embodiment of aquestionnaire.

FIG. 7 is a block diagram illustrating an embodiment of migrainetriggers and sensitivity thresholds.

FIG. 8A is a block diagram illustrating an embodiment of a questionnairedata structure.

FIG. 8B is a block diagram illustrating an embodiment of aquestionnaire.

FIG. 9A is a block diagram illustrating an embodiment of determining acompound variable associated with events having different temporalonsets.

FIG. 9B is a block diagram illustrating an embodiment of determiningcompound variables.

FIG. 10 is a block diagram illustrating an embodiment of a variableoccurring during a duration of an event.

FIG. 11 is a block diagram illustrating an embodiment of determiningmodel complexity.

FIG. 12 is a block diagram illustrating an embodiment of rankingvariables.

FIG. 13 is a block diagram illustrating an embodiment of associating oneor more variables with one or more groups of variables.

FIG. 14 is a block diagram illustrating an embodiment of a signalprocessing circuit.

FIG. 15 is a flow diagram illustrating an embodiment of a process forcollecting information.

FIG. 16 is a flow diagram illustrating an embodiment of a process fordetermining one or more association variables.

FIG. 17 is a flow diagram illustrating an embodiment of a process forproviding recommendation(s) and/or report(s).

FIG. 18 is a flow diagram illustrating an embodiment of a process forproviding one or more reports.

FIG. 19 is a block diagram illustrating an embodiment of a questionnairedata structure.

FIG. 20 is a block diagram illustrating an embodiment of a datastructure.

Like reference numerals refer to corresponding parts throughout theseveral views of the drawings.

DETAILED DESCRIPTION

Reference will now be made in detail to embodiments, examples of whichare illustrated in the accompanying drawings. In the following detaileddescription, numerous specific details are set forth in order to providea thorough understanding of the present invention. However, it will beapparent to one of ordinary skill in the art that the present inventionmay be practiced without these specific details. In other instances,well-known methods, procedures, components, and circuits have not beendescribed in detail so as not to unnecessarily obscure aspects of theembodiments.

Embodiments of one or more apparatuses, and related methods, forcollecting information and determining one or more association variablesare described. The information may be collected by asking a subset ofpre-determined questions in a set of pre-determined questions one ormore time during a data-collection time interval using the one or moreapparatuses.

The subset of pre-determined questions may be varied during thedata-collection time interval in accordance with configurationinstructions received by the one or more apparatuses. In someembodiments, the configuration instructions correspond to non-executableinstructions. The configuration instructions may be transmitted to theone or more apparatuses and answers to a subset of the pre-determinedquestions, selected in accordance with the configuration instructions,may be received from the one or more apparatuses using SMS messagesand/or email messages.

The subset of pre-determined questions may include pre-selected answersthat may be displayed for each question in at least a plurality ofpre-determined questions in the subset of pre-determined questions.Answering a respective pre-determined question may only involveselection if the respective answer to the respective pre-determinedquestion is different than the respective pre-selected answer. Thepre-selected answers may be selected in accordance with an answerhistory for one or more individuals and/or one or more groups ofindividuals. In some embodiments, the pre-selected answers maycorrespond to one or more default answers.

In some embodiments, at least one apparatus or device, such as apersonal digital assistant, a tablet computer, a Blackberry, a cellulartelephone, and/or a hand-held computer, containing the set ofpre-determined questions may be provided to at least one individual. Insome embodiments, at least the one individual may be provided a memorycard (such as a smart card, a subscriber identity module or SIMS card,and/or a card having ROM, FLASH or other memory) containing the set ofpre-determined questions. In some embodiments, at least one server maytransmit instructions corresponding to the subset of pre-determinedquestions to at least one computer. A browser in at least the onecomputer may render the instructions corresponding to the subset ofpre-determined questions. Communication with apparatuses, devices,computers, and/or servers may occur via a network, such as the Internet(also known as the World Wide Web), an Intranet, a local area network(LAN), a wide area network (WAN), a metropolitan area network (MAN),and/or a wireless network.

The one or more association variables may be determined and/oridentified, using one or more apparatuses, and/or the related methods,in accordance with one or more statistical relationships between one ormore temporal onsets corresponding to one or more events and patterns ofoccurrence of one or more variables and/or one or more compoundvariables. The one or more compound variables may be determined usingone or more variables in a set of variables. The one or more temporalonsets may include one or more onset times and/or one or more onsetsduring one or more time intervals.

A respective compound variable may correspond at least to a pattern ofoccurrence of a first variable during a first time interval precedingthe one or more temporal onsets and a pattern of occurrence of a secondvariable during a second time interval preceding the one or moretemporal onsets. The first time interval and/or the second time intervalmay be offset in time from the one or more temporal onsets. The firsttime interval may be different than the second time interval. In someembodiments, the respective compound variable may correspond to patternsof occurrence of three of more variables during corresponding timeintervals preceding the one or more temporal onsets.

Contributions from presence and absence information in the pattern ofoccurrence of the one or more compound variables may be included whendetermining the one or more statistical relationships. The pattern ofoccurrence for the respective compound variable, such as the pattern ofoccurrence of the first variable and the pattern of occurrence of thesecond variable, may include presence and absence information.

The one or more statistical relationships may be determined using anon-parametric analysis technique (which makes few assumptions about anexistence of a probability distribution function, such as a normaldistribution, corresponding to a population from which samples areobtained, or regarding independence of the variables and/or the compoundvariables) and/or a supervised learning analysis technique. The analysismay perform hypothesis testing to determine if the one or more temporalonsets and the one or more compound variables and/or one or morevariables are statistically independent (or dependent) in accordancewith a statistical significance criterion. In the process, the analysismay increase an effective signal-to-noise ratio in an underdeterminedproblem (i.e., sparse sampling in a multi-dimensional variable space) byrestricting a number of local fitting neighborhoods (i.e., a number ofrelevant variables and/or compound variables).

The one or more compound variables may be ranked in accordance with theone or more statistical relationships. The variables in the set ofvariables may be ranked in accordance with a number of occurrences ofthe variables in the compound variables having respective statisticalrelationships that approximately exceed the statistical confidencethreshold corresponding to a noise floor. The statistical confidencethreshold may be selected such that at least a subset of the ranking isapproximately stable.

One or more variables in the one or more compound variables, such as thefirst variable and the second variable, may be identified as the one ormore association variables. Additional association variables may beidentified by associating the one or more association variables with oneor more groups of association variables, including predetermined groupsof association variables. One or more recommendations may be provided toat least the one individual in accordance with the one or moreassociation variables. In some embodiments, at least the one individualmay be a healthcare provider (such as a physician, nurse, chiropractor,and/or an associated staff member), a parent, a guardian, and/or anindividual that has a disease. The one or more recommendations may beincluded in one or more reports.

The one or more association variables may, at least in part, trigger,initiate, and/or precipitate the one or more events (henceforth referredto as trigger). The one or more association variables may directly orindirectly cause the one or more events. Alternatively, the one or moreassociation variables may not directly or indirectly cause the one ormore events. The one or more association variables may enable the one ormore events. To make an analogy, in some embodiments the one or moreassociation variables may function as keys in one or more locks(receptors), allowing a spring-loaded door (corresponding, for example,to a biochemical predisposition) to open. Two or more associationvariables may work in conjunction with one another, i.e., at least theone individual may experience at least one event if at least the oneindividual is exposed to two or more association variables in closetemporal proximity, in a temporal sequence and/or in an ordered temporalsequence (i.e., a particular pattern of exposure to two or moreassociation variables). An effect of the association variables may becumulative. Exposure to a sufficient quantity of the respectiveassociation variable may trigger the one or more events, or exposure toquantities of two or more association variables may trigger the one ormore events. Be it cumulative and/or cooperative, the respectiveassociation variable may correspond to 5%, 10% 25%, 50% or more of atotal trigger for the one or more events. The one or more associationvariables may be specific to an individual and/or a group of two or moreindividuals. In some embodiments, the one or more events may correspondto an episodic increase in a severity of one or more symptoms associatedwith a disease, such as a chronic disease, and/or a disease condition inat least the one individual.

The disease may include a form of arthritis, rheumatoid arthritis, jointdisease, an auto-immune disorder, an immune-related disorder, aninflammatory disease, lupus, thyroid disease, gout, diabetes, chronicfatigue syndrome, insomnia, depression, a psychological disease,gastrointestinal disease, colitis, ulcerative colitis, inflammatorybowel disease, Crohn's disease, candida, celiac disease, irritable bowelsyndrome, one or more food allergies, one or more food sensitivities,morning sickness, menstrual cramps, chronic pain, back pain, facialpain, fibromyalgia, asthma, migraines, abdominal migraines, cyclicvomiting syndrome, cluster headaches, chronic headaches, tensionheadaches, another type of headaches, seizures, epilepsy,neurodermatitis, acne, psoriasis, adiposity, hypertonia, heart disease,hypertension, cardiovascular disease, arteriosclerosis, a form ofcancer, and/or acquired immune deficiency syndrome. The system andmethod may also be applied to patients have multiple illnesses, such ageriatric patients.

The embodiments of the apparatuses, and related methods, may be used tocollect and analyze information associated with time-varying phenomenahaving discrete epochs. The embodiments of the apparatuses, and relatedmethods, may also be used to identify one or more association variablesfor such time-varying phenomena, thereby allowing remedial action to betaken (if appropriate). Technical effects for the embodiments of theapparatuses, and related methods, may include displaying one or morequestionnaires, including a plurality of pre-selected answers, on atleast one display, transmitting and receiving collected informationusing a network, determining one or more statistical relationships in atleast one apparatus, identifying one or more association variables in atleast the one apparatus, transmitting and receiving one or morerecommendations and/or one or more reports using the network, and/ordisplaying the one or more recommendations and/or the one or morereports on at least one display.

The embodiments of the apparatuses, and related methods, may allowcollected data or information to be converted into actionableinformation, such as one or more recommendations. In some embodiments,providing the recommendations to one or more healthcare providers and/orat least the one individual that has the disease may help convert thisinformation into knowledge. The healthcare providers, who practicemedicine, may use the knowledge to aid the one or more individuals thathave the disease, including prescribing one or more diagnostic testsand/or one or more treatment modalities. In the hands of at least theone individual that has the disease, the information may motivatebehavior modification that may mitigate or reduce a severity and/orfrequency of one or more symptoms associated with the disease.

Attention is now directed towards embodiments of apparatuses, devices,computers, servers, and systems that may be used to implement thecollection of information, the statistical analysis and/or the providingof recommendations. FIG. 2 is a block diagram illustrating an embodimentof a system 200 for collecting data or information, analyzing theinformation, and/or for providing recommendations (for example, in oneor more reports). A network 214 couples servers 222 and optionaldatabases 220 (located in one or more additional computers, servers,and/or network attached storage devices) to first locations 210 andsecond locations 212. The network 214 may include the Internet (alsoknown as the World Wide Web), an Intranet, a local area network (LAN), awide area network (WAN), a metropolitan area network (MAN), and/or awireless network (including one or more cellular telephone networks,Bluetooth networks, Wi-MAX networks, and/or Wi-Fi networks using IEEE802.11a, 802.11b, 802.11g and/or 802.11n).

The first locations 210 may correspond to one or more individuals orhumans beings. In some embodiments, the one or more individuals may havebeen diagnosed as having the disease. At least one of the one or moreindividuals (henceforth referred to as a first individual) may interactwith at least one of computers 216 and devices 218. The devices 218 mayinclude one or more personal digital assistants, one or more tabletcomputers, one or more cellular telephones, one or more hand-heldcomputers, and/or a combination of two or more of these items. One ormore of the servers 222 may provide the subset of pre-determinedquestions one or more times during the data-collection time interval.Pre-determined questions may include questions that are determined priorto the beginning of the data-collection time interval. In someembodiments, the pre-determined questions may be generated for at leastthe first individual prior to the beginning of the data-collection timeinterval, for example, in accordance with an optional initial survey. Inexemplary embodiments, the data-collection time interval may beapproximately a fraction of a day (such as 1, 3, 4 or 6 hours), a day,several days, a week, a month, 2 months, 3 months, 4 months, 6 months, 9months, a year, several years, and/or a combination of one or more ofthese items.

In some embodiments, one or more of the servers 222 may provideinstructions for a web page corresponding to the subset ofpre-determined questions which are rendered in a browser. Theinstructions for the web page may include embedded JavaScriptinstructions that may be executed by one or more of the computers 216and/or devices 218. In some embodiments, one or more of the computers216 and/or devices 218 may already contain the subset of pre-determinedquestions or the set of pre-determined questions. Configurationinstructions, which may be non-executable, from the one or more servers222 may select the subset of predetermined questions. In otherembodiments, at least the first individual may be asked one or morequestions that are not pre-determined, such as one or more questionsthat may be dynamically generated in one or more of the servers 222.Such dynamically generated questions may be provided approximately inreal-time during the data-collection time interval.

At least the first individual may provide answers to the subset ofpre-determined questions one or more times during the data-collectiontime interval. The answers may be transmitted to one or more of theservers 222 and/or one or more of the optional databases 220. In someembodiments, the answers are transmitted at least in part using emailmessages and/or SMS messages, or only using email messages and/or SMSmessages. The email messages and/or SMS messages may be encrypted. Oneor more of the servers 222, in conjunction with information stored inone or more of the optional databases 220, may analyze the answers todetermine one or more statistical relations, the ranking of thevariables, and/or to identify the one or more association variables. Oneor more of the servers 222 may revise the subset of pre-determinedquestions that are provided and/or provide revised configurationinstructions, which may be non-executable, to at least one of thecomputers 216 and devices 218 in order to modify the subset ofpre-determined questions for at least the first individual. In someembodiments, the configuration instructions may be provided at least inpart using email messages and/or SMS messages, or only using emailmessages and/or SMS messages. The email messages and/or SMS messages maybe encrypted. The configuration instructions may be determined inaccordance with the answer history for at least a subset of the one ormore individuals, one or more groups of individuals, and/or defaultanswers to the subset of pre-determined questions that may be stored inat least one of the optional databases 220.

The second locations 212 may correspond to one or more healthcareproviders (such as a physician, nurse, chiropractor, and/or anassociated staff member), one or more parents, one or more guardians,and/or one or more additional individuals that have the disease(henceforth referred to as a second individual). In some embodiments,one or more of the servers 222, in conjunction with information storedin one or more of the optional databases 220, may provide one or morerecommendations in accordance with the one or more determinedassociation variables to at least the second individual. In someembodiments, one or more of the servers 222, in conjunction withinformation stored in one or more of the optional databases 220, mayprovide the one or more recommendations in accordance with the one ormore determined association variables to at least the first individualat one or more of the first locations 210. The one or morerecommendations may be in the form of one or more reports or documents,including soft or hard copies. In some embodiments, the recommendationsmay be provided by transmitting a data stream including therecommendations and/or transmitting a data stream including instructionscorresponding to the recommendations (for example, the instructions maycorrespond to one or more web-pages) by email, SMS, and/or by regularmail.

In other embodiments, at least the first individual may be asked one ormore questions that are provided by at least the second individual. Suchquestions from at least the second individual may be dynamicallygenerated and may be provided approximately in real-time during thedata-collection time interval. Answers to these and/or other dynamicallygenerated questions (such as those that may be provided by the one ormore servers 222) may be provided to at least the second individualand/or one or more servers 222 approximately in real-time or after atime delay. In some embodiment, at least the second individual mayprovide feedback and/or instructions to one or more of the servers 222that is based on the one or more recommendations. In some embodiments,the feedback and/or instructions may be used to revise the configurationinstructions. In some embodiments, the feedback and/or instructions maybe used to determine one or more additional pre-determined questionsthat are provided to at least the first individual.

While the system 200 has been shown with two computers 216 and twodevices 218 at the first locations 210, and two computers 216 at thesecond locations 212, there may be fewer or additional computers 216and/or devices 218. In addition, one or more individuals at the firstlocations 210 and/or the second locations 212 may share a computer, adevice, a set of computers, and/or a set of devices. Similarly, theremay be fewer or more servers 222 and/or optional databases 220. One ormore functions of one or more of the computers 216, devices 218, servers222, and/or optional databases 220 may be combined into a single item inthe system 200 and/or may be performed at one or more remote locationsin the system 200. One or more positions of one or more items in thesystem 200 may be changed. In some embodiments, the functions of the oneor more servers 222 and/or optional databases 220 may be performed inone or more of the computers 216 and/or one or more of the devices 218,for example, using one or more applications programs or modulesinstalled on one or more of the computers 216 or in one or moreremovable storage media in one or more of the computers 216. In someembodiments, the one or more applications programs or modules installedon one or more of the computers 216 or in one or more removable storagemedia in one or more of the computers 216 may be dedicated orstand-alone applications that function without interactions with one ofthe servers 222 or only occasionally interact with one or more of theservers 222.

FIG. 3 is a block diagram illustrating an embodiment of a server orcomputer 300, such as one of the computers 216 and/or servers 222 inFIG. 2. The server or computer 300 includes one or more processing units(CPUs) 310, at least one network or communications interface 322 forcommunicating with other computers, servers, devices, and/or databases,a memory device 324 with primary and secondary storage, at least oneoptional user interface 314, and one or more signal lines 312 forconnecting these components. The one or more processing units (CPUs) 310may support parallel processing and/or multi-threaded operation. Theoptional user interface 314 may have one or more displays 316, keyboards318, pointers 320 (such as a mouse), a touchpad (not shown), and/or avoice interface 308, including one or more speakers and/or microphones.The one or more displays 316 may include a touch screen (which maycombine at least one of the keyboards 318 with at least one of thedisplays 316). The one or more signal lines 312 may constitute one ormore communication buses. The network or communications interface 322may have a persistent communication connection.

The memory device 324 may include high speed random access memory and/ornon-volatile memory, including ROM, RAM, EPROM, EEPROM, FLASH, one ormore smart cards, one or more magnetic disc storage devices, and/or oneor more optical storage devices. The memory device 324 may store anoperating system 326, such as LINUx, UNIX, OS10, or WINDOWS, thatincludes procedures (or a set of instructions) for handling variousbasic system services for performing hardware dependent tasks. Thememory device 324 may also store procedures (or a set of instructions)in a network communications module 328. The communication procedures maybe used for communicating with one or more computers 216 (FIG. 2),servers 222 (FIG. 2), optional databases 220 (FIG. 2), and/or devices218 (FIG. 2). The communication procedures may include those for aparallel interface, a serial interface, an infrared interface,Bluetooth, Firewire (IEEE 1394A and/or IEEE 1394B), and/or a USBinterface (for example, USB-1 and/or USB-2 or High-Speed USB). Thecommunication procedures may include HyperText Transport Protocol (HTTP)to transport information using the Transmission ControlProtocol/Internet Protocol (TCP/IP), as well as a secure or encryptedversion of HTTP, such as Hypertext Transport Protocol over Secure SocketLayer (HTTPS), a Layer 2 Tunneling Protocol (L2TP), or another InternetProtocol Security, such as IPSEC.

The memory device 324 may also include the following elements, or asubset or superset of such elements, including a questionnaire module(or a set of instructions) 330, an encryption/decryption module (or aset of instructions) 340 (using, for example, pretty good privacy,symmetric encryption, and/or asymmetric encryption), a statisticalanalysis module (or a set of instructions) 342, a compound variablegenerator (or a set of instructions) 346, an optional signal processingmodule (or a set of instructions) 350, a report generator (or a set ofinstructions) 352 for formatting and providing recommendations andrelated information to at least one of the first individual and/or thesecond individual, pre-determined questions (or a set of instructions)354, pre-selected answers (or a set of instructions) 358, pattern ofoccurrence data 364 corresponding to one or mote events and/or one ormore variables, association variable(s) 366, pre-determined sets ofassociation variables 368, and/or an optional location module (or a setof instructions) 370 for determining a location of one or more computers216 (FIG. 2) and/or one or more devices 218 (FIG. 2) (for example, usingan IP address, a global positioning system, and/or remote localizationcapability associated with a portable device such as a cellulartelephone).

The questionnaire module 330 may include a client communication module(or a set of instructions) 332 and/or a question pattern module (or aset of instructions) 336 for providing the subset of pre-determinedquestions to at least the first individual. The client communicationmodule 332 may include a web-page generator module (or a set ofinstructions) 334 that generates instructions corresponding to one ormore web pages, including HyperText Mark-up Language (HTML), eXtensibleMark-up Language (XML), Java, JavaScript, Perl, PHP, and/or .NET. Thequestion pattern module 330 may include a configuration instructionsmodule (or a set of instructions) 338 for providing instructions thatselect the subset of pre-determined questions. The statistical analysismodule 342 may include a ranking module (or a set of instructions) 344.The compound variable generator 346 may include a threshold module (or aset of instructions) 348 for determining if one or more entries in thepattern of occurrence data 364 for at least one variable correspond to apresence or an absence. The pre-determined questions 354 may include oneor more question modules (or a set of instructions) 356. Thepre-selected answers 358 may include answer history 360 for one or moreindividuals and/or one or more groups of individuals, and/or defaultanswers (or a set of instructions) 362.

In some embodiments, the server or computer 300 may communicate with oneor more optional physiological monitors 372. Communication may be via acable (such as USB), infrared, Firewire and/or wireless (such as Wi-Fior Bluetooth). In an alternate embodiment, at least the first individualmay manually enter physiological data from the one or more optionalphysiological monitors 372 using one of the components in the optionaluser interface 314. In some embodiments, the physiological data may beentered using a binary search procedure corresponding to a series ofquestions, such as, “Is the physiological data value less than 0.5?,”“Is the physiological data value less than 0.25?,” “Is the physiologicaldata value greater than 0.375?,” and so on until a desired precision isobtained. (A similar binary search procedure may be used to answer oneor more pre-determined questions in the subset of pre-determinedquestions.) Communication with the one or more optional physiologicalmonitors 372 may be at discrete times or it may be continuous. The oneor more optional physiological monitors 372 may include anelectroencephalogram monitor (such as a respective Holter monitor), anelectrocardiogram monitor (such as the respective Holter monitor), anelectromyleogram monitor, an inflammatory response monitor, arespiratory monitor (such as the Air Watch II) of variables such as peakexpiratory flow and/or a forced expiratory volume in 1 second, a bloodglucose monitor, a blood pressure monitor, a thermometer, a vital signmonitor (such as those for pulse or respiration rate), a galvanometricresponse monitor, and/or a reflex arc monitor.

Instructions in the modules in the memory device 324 may be implementedin a high-level procedural language, an object-oriented programminglanguage, and/or in an assembly or machine language. The programminglanguage may be complied or interpreted, i.e, configurable or configuredto be executed by the one or more processing units 310. In addition, theserver or computer 300 may include fewer or additional executableprocedures, sub-modules, tables, and/or other data structures (notshown). In some embodiments, additional or different modules and datastructures may be used and some of the modules and/or data structureslisted above may not be used. In some embodiments, the functions of twoor more modules may be combined in a single module. In some embodiments,implementation of functionality of the server or computer 300 may beimplemented more in hardware and less in software, or less in hardwareand more in software, as is known in the art.

Although the server or computer 300 is illustrated as having a number ofdiscrete items, FIG. 3 is intended more as a functional description ofthe various features which may be present in the server or computer 300rather than as a structural schematic of the embodiments describedherein. In practice, and as recognized by those of ordinary skill in theart, the functions of the server or computer 300 may be distributed overa large number of servers or computers, with various groups of theservers or computers performing particular subsets of the functions.Items shown separately in the server or computer 300 may be combined,some items may be separated and/or additional items may be added. Theapparatuses and methods disclosed may be implemented in hardware and/orsoftware. In alternate embodiments, some or all of the functionality ofthe server or computer 300 may be implemented in one or more applicationspecific integrated circuits (ASICs) and/or one or more digital signalprocessors (DSPs).

FIG. 4 is a block diagram illustrating an embodiment of a computer 400,such as one of the computers 216 (FIG. 2). The memory device 324 mayinclude a browser module (or a set of instructions) 410 for renderingweb-page instructions, a transmission/receipt module (or a set ofinstructions) 414 for handling information such the subset ofpre-determined questions and corresponding answers, an optional voicerecognition module (or a set of instructions) 416, a display module (ora set of instructions) 418 for displaying the subset of predeterminedquestions and/or the pre-selected answers to at least the firstindividual, configuration instructions 420, and/or a report(s) module(or a set of instructions) 422 for formatting and presentingrecommendations and related information to at least one of the firstindividual or the second individual. The browser module 410 may includeinstructions corresponding to one or more web pages 412. The computer400 may optionally perform at least a portion of the analysis, such asthe determining at least some of the statistical relationships. Thecomputer 400 may store pattern of occurrence data 364 corresponding toone or more events and/or one or more variables. The pattern ofoccurrence data 364 may be stored temporarily as the subset ofpre-determined questions are answered over at least a portion of thedata-collection time interval. For example, answers for a respective daymay be transmitted at night. In some embodiments, the pattern ofoccurrence data 364 may be communicated to one or more of the servers222 (FIG. 2) and/or optional databases 220 (FIG. 2) approximately inreal-time, for example, as respective pre-determined questions areanswered. In some embodiments, the computer 400 may communicate with oneor more optional physiological monitors 372.

Although the computer 400 is illustrated as having a number of discreteitems, FIG. 4 is intended more as a functional description of thevarious features which may be present in the server or computer 400rather than as a structural schematic of the embodiments describedherein. In practice, and as recognized by those of ordinary skill in theart, the functions of the server or computer 400 may be distributed overa large number of servers or computers, with various groups of theservers or computers performing particular subsets of the functions.Items shown separately in the computer 400 may be combined, some itemsmay be separated and/or additional items may be added. The apparatusesand methods disclosed may be implemented in hardware and/or software. Inalternate embodiments, some or all of the functionality of the server orcomputer 400 may be implemented in one or more ASICs and/or one or moreDSPs.

FIG. 5 is a block diagram illustrating an embodiment of a device 500,such as one of the devices 218 (FIG. 2). The device 500 may include acellular telephone, a personal digital assistant, a tablet computer, aBlackberry, a hand-held computer, and/or a combination of two or more ofthese items. In some embodiments, the device 500 may be an implantablemedical device that collects and communicates data for at least thefirst individual. The device 500 may include one or more dataprocessors, video processors and/or processors 510, at least onecommunications interface 512 for communicating with other computers,servers, devices and/or databases, a first memory device 516 withprimary and/or secondary storage, a second optional memory device 524that may be removable, at least the one user interface 314, a sensor508, and one or more signal lines 312 for connecting these components.The one or more data processors, video processors and/or processors 510may support parallel processing and/or multi-threaded operation. Theuser interface may have one or more displays 316, keyboards 318,pointers 320 (such as a mouse or a stylus), a touchpad (not shown),and/or a voice interface 308 including one or more speakers and/ormicrophones. The one or more displays 316 may include a touch screen(which may combine at least one of the keyboards 318 with at least oneof the displays 316). The one or more signal lines 312 may constituteone or more communication buses. The communications interface 512 mayinclude a radio transceiver 508 for converting signals from baseband toone or more carrier bands and/or from one or more carrier bands tobaseband. The communications interface 512 may have a persistentcommunication connection. The device 500 may include a power source 514,such as a battery or a rechargeable battery, for supplying power to oneor more of these components. The sensor 508 may be an imaging element,such as CCD array, for capturing one or more images (such as pictures).

The memory device 516 may include high speed random access memory and/ornon-volatile memory, including ROM, RAM, EPROM, EEPROM, FLASH, one ormore smart cards, one or more magnetic disc storage devices, and/or oneor more optical storage devices. The memory device 516 may store anembedded operating system 518, such as LINUX, UNIX, OS10, PALM orWINDOWS, or a real-time operating system (such as VxWorks by Wind RiverSystem, Inc.) suitable for use in industrial or commercial devices. Theoperating system 518 may includes procedures (or a set of instructions)for handling various basic system services for performing hardwaredependent tasks, including password and/or biometric securityauthentication. The memory device 516 may also store procedures (or aset of instructions) in a communications module 520.

The communication procedures in the communications module 520 may beused for communicating with one or more computers 216 (FIG. 2), servers222 (FIG. 2), optional databases 220 (FIG. 2), and/or devices 218 (FIG.2). The communication procedures may include those for a parallelinterface, a serial interface, an infrared interface, Bluetooth,Firewire (IEEE 1394A and/or IEEE 1394B), and/or a USB interface (forexample, USB-1 and/or USB-2 or High-Speed USB). The communicationprocedures may include one or more protocols corresponding to a GlobalSystem for Mobile Telecommunications (GSM), Code Division MultipleAccess (CDMA), a Short Message Service (SMS), an Enhanced MessagingService (EMS), a Multi-media Message Service (MMS), a General PacketRadio Service (GPRS), a Wireless Application Protocol (WAP), instantmessaging, email, TCP/IP, and/or a voice over internet protocol (VoIP).Note that SMS supports communication of up to 160 characters using, forexample, text messaging. Email may utilize an email addresscorresponding to a subscriber's 10-digit telephone number, such as1234567890@messaging.carrier.com, where ‘carrier’ may be a cellulartelephone provider such as Cingular. EMS includes text formatting, andsupports communication of simple black and white images, as well assound tones. MMS supports communication of a wide variety of media fromtext to video.

The memory device 516 may also include the following elements, or asubset or superset of such elements, including the optional browsermodule (or a set of instructions) 410, the transmission/receipt module(or a set of instructions) 414, the encryption/decryption module (or aset of instructions) 340, the optional voice recognition module (or aset of instructions) 416, an optional voice replication module (or a setof instructions) 522 for asking at least some of the subset ofpre-determined questions using the voice interface 308, the displaymodule (or a set of instructions) 418, the configuration instructions420, the optional statistical analysis module (or a set of instructions)342, the optional compound variable generator (or a set of instructions)346, the optional signal processing module (or a set of instructions)350, the optional report(s) module (or a set of instructions) 422, theoptional answer history 360, and/or the optional pattern of occurrencedata 364.

The optional browser module 410 may include instructions correspondingto one or more web pages 412. The optional statistical analysis module342 may include the optional ranking module (or a set of instructions)344. The optional compound variable module 346 may include the optionalthreshold module (or a set of instructions) 348. The device 500 mayoptionally perform at least a portion of the analysis, such asdetermining at least some of the statistical relationships. Theencryption/decryption module 340 may include encryption/decryption thatis supported in the GSM and/or CDMA protocols. In some embodiments, theencryption/decryption module 340 may include a virtual private network(VPN) tunneling application.

The optional pattern of occurrence data 364 may be stored temporarily asthe subset of pre-determined questions are answered over at least aportion of the data-collection time interval. For example, answers for arespective day may be transmitted at night. In some embodiments, theoptional pattern of occurrence data 364 may be communicated to one ormore of the servers 222 (FIG. 2) and/or optional databases 220 (FIG. 2)approximately in real-time, for example, as respective pre-determinedquestions are answered.

In an exemplary embodiment, the configuration instructions 420 and theanswers to the subset of pre-determined questions may be communicatingusing one or more SMS text messages and/or email messages. In someembodiments, only SMS text messages and/or email messages are used. Theuse of SMS text messaging and/or email messaging may result in costsavings associated with establishing accounts and/or with thecommunication. Receipt of a respective SMS text message by an enddestination, such as one of the servers 222 (FIG. 2), may be confirmedusing a handshake message (such as another SMS message). Upon receipt ofsuch a handshake message, the device 500 may erase and/or deleteinformation that was transmitted in the original SMS text message (suchas one or more answers to the subset of pre-determined questions or atleast a portion of the optional pattern of occurrence data 364).

In some embodiments, at least the first individual may use the device500 to collect information that may be subsequently used to answer oneor more of the subset of pre-determined questions. For example, thesensor 508 may be used to take a picture of a menu, a table of contents,and/or one or more medicines consumed. Or an audio file listing itemsconsumed during a meal or snack may be recorded. The collectedinformation may be processing in the device 500, or in one or moreremote computers and/or one or more servers, using image processing,text recovery/identification, and/or speech recognition (using, forexample, the optional voice recognition module 416. In some embodiments,the device 500 may communicate with one or more optional physiologicalmonitors 372.

In some embodiments, the device 500 may provide at least the firstindividual with at least one reminder (such as a reminder to take amedicine at a respective time) using one or more messages transmitted tothe device 500 and/or using one or more pre-stored messages in thedevice 500 that may be enabled by the configuration instructions 420. Atleast the one reminder may be provided using the voice replicationmodule 522 and the voice interface 308.

The optional memory device 524 may include one or more FLASH drives,ROMs, memory sticks, optical storage media (such as rewritable or ROMCDs and/or DVDs), smart cards, SIMS cards, secure digital (SD) cards(compatible with devices that use a PALM embedded operating system),multimedia cards (MMCs), magnetic disc storage devices (such as discdrives), and/or magnetic media (such as floppy discs). The optionalmemory device 524 may also include the following elements, or a subsetor superset of such elements, including the pre-determined questions (ora set of instructions) 354, the pre-selected answers 358 (or a set ofinstructions), and/or the optional account information 526. Thepre-determined questions 354 may include the one or more questionmodules (or a set of instructions) 356. The pre-selected answers 358 mayinclude optional default answers (or a set of instructions) 362. Theoptional account information 526 may include at least one carrieraccount number (for an Internet service provider, a cellular telephoneprovider, and/or a wireless services provider) and/or at least onetelephone number that may enable at least the first individual toreceive the configuration instructions 420 and to transmit the answersto the subset of pre-determined questions. The optional accountinformation 526 may allow at least the first individual to communicatewith one or more providers of services that determine one or moreassociation variables.

In some embodiments, at least first individual is provided with theoptional memory device 524, which may be installed in the device 500.One or more additional optional memory devices may also be provided toat least the first individual at later times during the data-collectiontime interval. The one or more additional optional memory devices mayinclude revised pre-determined questions 354 and/or revised pre-selectedanswers 358.

In some embodiments, the optional memory device 524 is a SIMS card, anSD card, and/or a memory card and the device 500 is a cellulartelephone. By including the optional account information 526,communication using at least the one carrier account number and/or atleast the one telephone number may avoid the so-called SIMS card lockthat prevents modification of such information in a cellular telephonethat is issued by a cellular telephone provider to a subscriber. Thismay allow one or more modules or applications to be installed and/orexecuted on the cellular telephone independently of the cellulartelephone provider. Address book and/or additional telephone numbers(such as a list of frequently used telephone numbers) on an existingSIMS card, SD card, and/or memory card for at least the first individualmay be copied on to the optional memory device 524. Alternatively, atleast the first individual may at least temporarily provide the existingSIMS card, SD card, and/or memory card, which may allow the address bookand/or the additional telephone, numbers to be copied on to a newoptional memory device 524 that may be provided to at least the firstindividual.

In some embodiments, the pre-determined questions 354 and/or thepre-selected answers 358 may be copied on to the optional memory device524 or the existing SIMS card, SD card, and/or memory card in a cellulartelephone. Service, such as collecting the information, may be providedin conjunction with or separately from one or more cellular telephoneservice providers. In some embodiments, revised pre-determined questions354 and/or revised pre-selected answers 358 may be transmitted to andstored on the optional memory device 524 on one or more occasions duringthe data-collection time interval.

In some embodiments, at least the first individual owns or rents thedevice 500, thereby reducing a cost associated with collectinginformation, as well as a cost and complexity associated with supportingand maintaining hardware in the field. In some embodiments, at least thefirst individual is provided, either temporarily or permanently, withthe device 500.

The modules and/or some components in the memory device 516 may bearranged in a protocol stack, including a physical layer, a link layer,a network layer, a transport layer, and/or an application layer. In analternate embodiment, the protocol stack may include the network layer,the transport layer, a security layer, a session transaction layer,and/or the application layer. Instructions in the modules in the memorydevice 516 may be implemented in a high-level procedural language, anobject-oriented programming language, and/or in an assembly or machinelanguage. The programming language may be complied or interpreted, i.e,configurable or configured to be executed by one or more processors 510.In addition, the device 500 may include fewer or additional executableprocedures, sub-modules, tables and other data structures (not shown).In some embodiments, additional or different modules and data structuresmay be used and some of the modules and/or data structures listed abovemay not be used. In some embodiments, the functions of two or moremodules may be combined in a single module. In some embodiments,implementation of functionality of the device 500 may be implementedmore in hardware and less in software, or less in hardware and more insoftware, as is known in the art.

Although the device 500 is illustrated as having a number of discreteitems, FIG. 5 is intended more as a functional description of thevarious features which may be present in the device 500 rather than as astructural schematic of the embodiments described herein. In practice,and as recognized by those of ordinary skill in the art, the functionsof the device 500 may be distributed over a large number of servers orcomputers, with various groups of the servers or computers performingparticular subsets of the functions. Items shown separately in thedevice 500 may be combined, some items may be separated and/oradditional items may be added. One or more items or modules in thememory device 516, such as the configuration instructions 420, may bestored in the optional memory device 524 and vice versa. The apparatusesand methods disclosed may be implemented in hardware and/or software. Inalternate embodiments, some or all of the functionality of the device500 may be implemented in one or more ASICs and/or one or more DSPs.

Attention is now directed towards embodiments of the questionnaire andformats for displaying information contained in embodiments of thequestionnaire. As noted previously, the subset of pre-determinedquestions may be provided (for example, displayed) to at least the firstindividual along with respective pre-selected answers for each questionin at least a plurality of the pre-determined questions in the subset ofpredetermined questions. In this way, answering a respectivepre-determined question may involve selection if a respective answer tothe respective pre-determined question is different than a respectivepre-selected answer. The pre-selected answers may be selected inaccordance with an answer history, such as the answer history 360 (FIG.3), and/or default answers, such as the default answers 362 (FIG. 3).FIGS. 6A-6C illustrate embodiments of a questionnaire includingpre-determined questions and pre-selected answers.

FIG. 6A is a block diagram illustrating an embodiment of a questionnaire600, such as a questionnaire module (which may contain related questionsin a category of questions). Questionnaire modules are discussed furtherbelow with reference to FIGS. 8A and 8B. In the questionnaire 600,several primary questions 610, including pre-selected answers 616 andalternate answers 618, are displayed in a window 608. The window may bea graphical user interface, such as a dialog box or window, and it maybe displayed on a display, such as the display 316 (FIG. 4). A left edgeof at least a plurality of the primary questions 610 may be aligned withalignment 612-1 such that the primary questions 610 are arranged in acolumn. A left edge of at least a plurality of the pre-selected answers616 may be aligned with alignment 612-3 such that the pre-selectedanswers 616 are arranged in a column. A left edge of at least aplurality of the alternate answers 618 may be aligned with alignment612-4 such that the alternate answers 618 are arranged in a column. Inother embodiments, a right-edge, a center or another position in theprimary questions 610, the pre-selected answers 616 and/or the alternateanswers 618 may be used for purposes of alignment.

If the pre-selected answers 616 are correct, at least the firstindividual may select a next 624 icon at the bottom of the window 608(for example, by positioning a cursor over the accept 624 icon and leftclicking on a mouse) to accept the pre-selected answers 616 and requestanother window (unless the questionnaire 600 is completed) withadditional pre-determined questions and/or pre-selected answers.Selection of one or more of the alternate answers 618, such as alternateanswer 618-1, may occur if a corresponding pre-selected answer 616-1 isnot the correct answer for a corresponding primary question, such asprimary question 610-1.

If one or more of the alternate answers 618 are selected, secondaryquestions 620 may be displayed and/or enabled (i.e., at least the firstindividual may be able to modify the answer to a secondary question,such as secondary question 620-1). In some embodiments, when analternate answer, such as alternate answer 618-1 is selected, adisplayed color of one or more of one or more previously displayedsecondary questions 620 may be changed, for example, from grey to blackor from white to black. Note that some primary questions 610 may nothave one or more associated secondary questions 620 associated withthem. The secondary questions 620 may dependent on or may be conditionalon the answers to corresponding primary questions 610. A left edge of atleast a plurality of the secondary questions 620 may be aligned withalignment 612-2 such that the secondary questions 620 are arranged in acolumn. In other embodiments, a right-edge, a center or another positionin the secondary questions 620 may be used for purposes of alignment.The alignment 612-2 may be offset 614 from the alignment 612-1.

In the questionnaire 600, the primary questions 610 may be categoricalor discrete questions, i.e., having answers such as ‘yes’ or ‘no’. Insome embodiments, the pre-selected answers 616 may include both ‘yes’and ‘no’ responses. As a consequence, the pre-selected answers 616 mayalternate between ‘yes’ and ‘no’ for different primary questions 610. Inan alternate embodiment, ‘yes’ answers may be arranged in a first columnand ‘no’ answers may be arranged in a second column, such that aposition for the pre-selected answers 616 and the alternate answers 618may varying between the first column and the second column depending onthe primary questions 610. In the questionnaire 600, the secondaryquestions 620 may include categorical questions as well as one or moreordered categorical secondary question 620-2. (A left edge of thealternate answer 618-4 may be aligned with alignment 612-5. In otherembodiments, a right-edge, a center or another position in the alternateanswer 618-4 may be used for purposes of alignment.) In an orderedcategorical question, there is an ordering between values (such asanswers of ‘small’, ‘medium,’ or ‘large’) but a scale or metric valuemay vary (a difference between ‘medium’ and ‘small’ may be differentthan a difference between ‘large’ and ‘medium’). In some embodiments, anordered categorical question may have a pre-selected answer, such aspre-selected answer 616-3, that is in a different column than otherpre-selected answers 616. This may be useful when the ordered categoriesare time intervals, such as morning, afternoon, etc., and thepre-selected answer is not the left-most ordered category. Rather thanrearranging the ordered categories, the pre-selected answer 616-3 may bein a different column.

In some embodiments, the primary questions 610 and the secondaryquestions 620 may include categorical, ordered categorical and/orquantitative questions. Quantitative questions have answers that arecontinuous variables. Answers to quantitative questions may bepartition, for example using one or more thresholds or threshold values,to generate categorical or ordered categorical answers. In someembodiments, answers to one or more quantitative questions may be bandlimited prior to partitioning to reduce or eliminate aliasing.Categorical or ordered categorical answers may be converted intocontinuous answers using interpolation (such as minimum bandwidthinterpolation), subject to the limitations associated with the Nyquistsampling criterion.

In the questionnaire 600, the window 608 may include one or more helpicons 632, a home icon 622 to return to a master page in thequestionnaire 600, a jump icon 626 to save answers and skip to anotherwindow, an exit icon 628 to save answers and exit the questionnaire 600.While the questionnaire 600 includes three primary questions 610 andthree secondary questions 620, there may be fewer or more of either typeof question. In some embodiments, a respective question may have one ormore answers.

FIG. 6B is a block diagram illustrating an embodiment of a questionnaire640. Selection of a respective alternate answer, such as alternateanswer 618-6 (FIG. 6A), to one of the primary questions 610 (FIG. 6A) orsecondary questions 620 (FIG. 6A) may lead to window 652 beingdisplayed. The window 652 may include one or more help icons 664, andone or more items 654 with one or more quantities 658 and units 660(henceforth collectively referred to as entries) during correspondingtime intervals 656. At least the first individual may accept the entriesusing an accept icon 662 after making modifications (if any).Modifications may be made to one or more entries by positioning a cursorover an entry and manually typing one or more new values and/or by leftclicking on the mouse with the cursor over the entry and selecting a newvalue from a list box (a static object in the window 652) that appearswhen the mouse is positioned over it and/or a content-dependent list ormenu (a dynamic object) that may appear as a separate window when themouse is positioned over it. A list box and/or a content-dependent listor menu may include related objects and/or items, such as thosecorresponding to a category of items. In some embodiments, the entriesin the window 652 may blink to indicate that at least the firstindividual may modify one or more of them. In an exemplary embodiment,the items 654 may include pharmacological agents, prescription drugs,vitamins, herbs, supplements and/or recreational or illicit drugs(henceforth referred to as pharmacological agents). The quantities 658and units 660 may correspond to dosages. The time intervals 656 may be afraction of a day, such as approximately 1, 2, 3, 4, 6, 8, and/or 12hours. Thus, 50 mg of a drug taken in the morning (such as between 6 AMand 11.59 AM), may correspond to a quantity 658-1 of ‘50’ and a unit660-1 of ‘mg’. The entries in the window 652 may be pre-selected basedon the answer history, default answers, and/or answers to one or morequestions in the optional initial survey, which may include the usage ofpharmacological agents (times and dosages) by at least the firstindividual. The optional initial survey may be conducted prior to or atthe beginning or the data-collection time interval during which thesubset of pre-determined questions corresponding to the questionnaireare asked.

FIG. 6C is a block diagram illustrating an embodiment of a questionnaire670. A window 682 may include a table 688 including one or more items684 and/or a list 692 including one or more categories 690. Each of theitems 684 may have ordered categorical answers such as quantities 686.For example, quantity 686-1 may be ‘less than usual’, quantity 686-2 maybe ‘usual’, quantity 686-3 may be ‘more than usual’. One or morerespective quantities 686, such as the quantity 686-1, for one or morerespective items 684, such as the item 684-1, may be pre-selected.Categories 690 may be list boxes, and/or content-dependent lists ormenus. The categories 690 may correspond to food or beverage categories,such as vegetables, fruits, meats, etc.

In an exemplary embodiment, the window 682 may be used to collectinformation corresponding to one or more meals or snacks, such as foodseaten or beverages consumed. In some embodiments, snacks eaten betweenmeals may be included in the nearest previous meal. For example, a snackafter dinner and before breakfast may be included with the entries fordinner. The items 684 may be included in accordance with answers to oneor more pre-determined questions in the subset of predeterminedquestions during a current questionnaire session (for example, inanother questionnaire module) and/or may be previously consumed foods(which may be stored in an answer history, such as the answer history360 in FIG. 3, corresponding to one or more previous answer sessions,i.e., on one or more earlier occasions). The one or more pre-selectedquantities may be in accordance with the answer history. The previouslyconsumed foods may be the most common foods (for example, a top 10 list)consumed by at least the first individual. At least one of thecategories 690 may include additional consumed foods and/or previouslyconsumed foods for at least the first individual that are not displayedin the table 688. The items 684 may include food brand and/or foodcategory (such as Italian) information. In some embodiments, food brandand/or food category may be displayed using icons (not shown) situatedbetween the items 684 and the quantities 686.

One or more items 684 may be selected by clicking on one or more radialbuttons 680 (for example, positioning the mouse over a respective itemand left clicking the mouse). A displayed color of one or more selecteditems may be changed, for example, from grey to black. In someembodiments, the window 682 may be refreshed such that selected itemsare displayed starting at the top of the table 688 and items that arecurrently not selected are displayed below the selected items, i.e.,towards the bottom of the table 688. Additional items for inclusion inthe table 688 may be selected from one or more categories 690 and/or maybe manually entered using one or more optional manual entry boxes 694.

Selecting a category, such as category 690-1, may lead to acontent-dependent list or menu being displayed in a separate dialog boxor window. Entries selected from one or more categories 690 may bedisplayed in the table 688 as additional items with pre-selectedquantities and/or pre-selected radial buttons. The window 682 may berefreshed and/or re-ordered when one or more of such entries aredisplayed. At least the first individual may modify, as needed, one ormore pre-selected quantities for one or more of such entries displayedin the table 688 (for example, if one or more of the pre-selectedquantities did not correspond to one or more actual quantities consumedby at least the first individual). In some embodiments, entries in thewindow 670 that may be modified, such as the one or more pre-selectedquantities and/or the optional manual entry box 694, may blink toindicate that at least the first individual may modify one or more ofthem. Selecting an addition 698 icon may display a window that allows atleast the first individual to add one or more entries to one or morecategories 690. Alternative, the separate dialog box or window for arespective content-dependent list that has been selected may include amanual entry option so at least the first individual may add one or moreentries to the respective content-dependent list. The optional manualentry boxes, such as the optional manual entry box 694, in the table 688may be used to enter one or more new items, food brands, and/or foodcategories. Corresponding quantities may be entered using the quantities686. While the questionnaire 670 shows one optional manual entry box694, in some embodiments there may be additional manual entry boxes inone or more additional rows. Each row may be used to enter at least onenew item.

In embodiments where windows in the questionnaire 670 are provided inone or more web pages, JavaScript instructions included with the one ormore web pages may allow the table 688 to be updated without blinkingthe displayed window 682, i.e., without transmitting revised web pageinstructions from a remote server or computer, such as the server orcomputer 300 (FIG. 3). In the questionnaire 670, the window 682 mayinclude one or more help icons 696, the home icon 622, the next icon624, the jump icon 626 and/or the exit icon 628. In some embodiments,the window 682 may include additional or fewer items 684 and/orcategories 690.

A better understanding of the questionnaire and the determining of oneor more association variables (described further below with reference toFIGS. 9-14) may be provided by considering application to a class ofproblems, such as those associated with one or more diseases. Migrainesare used as an illustrative example. In this example, at least the firstindividual may be a migraine patient. In some embodiments, migraines mayinclude probable migraine, also referred to as migrainous, in whichpatients exhibit migraines minus one migraine symptom (which arediscussed below).

Migraine is a neurovascular disorder characterized by a family ofsymptoms that often include severe, recurring headache usually onone-side of the head. Migraine attacks are debilitating and have aduration that may last from several hours to days. During attacks, manypatients also exhibit sensitivity to environmental stimuli, such aslight and sound, and/or experience nausea or vomiting. Somecharacteristics of migraines, with and without aura, are summarized inTables I and II. Migraines typically follow a cycle, including aninitial or prodrome phase during which premonitory symptoms (discussedfurther below with reference to FIG. 8A) may be present, an aura phase(for patients that have migraine with aura) during which visualdisturbances may be present, a resolution or recovery phase, and anormal (i.e., non-migraine) phase.

TABLE I Some characteristics of migraine without aura A. Headacheattacks lasting 4-72 hours (untreated or unsuccessfully treated). B.Headache has at least two of the following characteristics: 1.Unilateral location; 2. Moderate or severe pain intensity; 3. Pulsatingquality; 4. Aggravated by or causing avoidance of routine physicalactivity (for example, walking or climbing stairs). C. During headacheat least one of the following: 1. Nausea and/or vomiting; 2. Lightsensitivity (photophobia) and sound sensitivity (phonophobia).

TABLE II Some characteristics of migraine with aura A. Aura consistingof at least one of the following, but no motor weakness: 1. Fullyreversible visual symptoms including positive features (for example,flickering lights, spots or lines) and/or negative features (such as,loss of vision); 2. Fully reversible sensory symptoms including positivefeatures (such as, pins and needles) and/or negative features (such as,numbness); 3. Fully reversible dysphasic speech disturbance. B. At leasttwo of the following: 1. Homonymous visual symptoms and/or unilateralsensory symptoms; 2. At least one aura symptom develops gradually over≧5 minutes and/or different aura symptoms occur in succession over ≧5minutes; 3. Each symptom lasts ≧5 and ≦60 minutes. C. Headachefulfilling criteria B-D for migraine without aura (Table I) beginsduring the aura or follows aura within 60 minutes.

The medical approach to managing migraine headaches is typicallythree-pronged, including acute therapy, preventive therapy, andidentification and avoidance of migraine triggers. Acute therapyincludes administering acute or prophylactic pharmacological agents,such as painkillers or analgesics, (for example, aspirin, acetaminophenor naproxen), ergotamine, dihydroergotamine, and/or a new class ofmedications known as “triptans” (selective serotonin 5-hydroxytryptamineor 5-HT receptor agonists, such as imitrex and maxalt), which aremigraine-specific medications that may treat the entire migrainecomplex, relieving the head pain, nausea, vomiting, and associated lightand sound sensitivity, typically within 1-2 hours. Most patients withmigraine are prescribed one or more forms of acute therapy.

Preventive therapy includes pharmacological agents or medications takenon a daily basis to reduce migraine headache frequency (a number ofheadaches during a time period) and severity (for example, a rating ofheadache pain by a patient). These pharmacological agents may be takenwhether a migraine headache is present or not. Prevention strategies aretypically employed for patients who suffer from one or more migraineheadaches per week. Only a minority of patients require this form oftherapy.

Identification and avoidance of migraine triggers is typically amainstay in the treatment of patients suffering from migraine. Ifpatients successfully avoid their migraine triggers, migraine headachefrequency and severity may be improved. (Note that some migrainetriggers, such as certain hormones, may be intrinsic or internal to thepatient. As such, the patient may still have spontaneous migraineattacks even if he or she successfully avoids his or her dominantmigraine triggers.) Identifying migraine triggers, however, remainschallenging and is often a source of frustration for patients andhealthcare providers. This is partly an outgrowth of the apparentcomplexity of migraine triggers. A myriad of probable or putativemigraine triggers are thought to exist. It has been hypothesized thatthe migraine triggers may vary significantly from one patient toanother, may vary within a respective patient (as discussed below withreference to FIG. 7, the respective patient's sensitivity threshold fora respective trigger may vary as a function of time), may depend on aquantity of exposure, and/or may depend on exposure to two or moretriggers in close temporal proximity.

In addition, current approaches for screening for migraine triggers maypose challenges. Typically, patients are given paper diaries and areasked to list what they think may have triggered an attack on arespective day. This approach may rely on the patients' recall ofevents, as diaries are often filled in days after a migraine attack, andmay therefore miss an exposure to migraine triggers. Patients may alsoassign a cause when one may not exist, or patients may assign blame toan incorrect variable(s). The apparent complexity of migraine triggersmay compound these difficulties.

FIG. 7 is a block diagram illustrating an embodiment 700 of migrainetriggers and sensitivity thresholds. A plurality of variables 712,corresponding to migraine triggers, each having a length correspondingto an amount of exposure 714 during a time interval are illustrated.Sensitivity thresholds 710 illustrate several effective sensitivitiesfor at least the first individual. At any given time, one or more of thesensitivity thresholds 710 may be operative for one or more of thevariables 712. The sensitivity thresholds 710 may vary as a function oftime. Such variation may occur slowly, for example, over a period ofmonths or even years. Variables 712 that exceed one of the sensitivitythresholds 710 corresponding to a current sensitivity for at least thefirst individual, such as variable B 712-2 and sensitivity threshold710-2, may trigger a migraine attack. Alternatively, combinations ofvariables 712, such as variable C 712-3 and variable F 712-6, may exceeda sensitivity threshold 710-3 and trigger a migraine. The combinationsmay be cumulative, may correspond to variables 712 that occur in closetemporal proximity, may correspond to respective temporal sequences ofvariables 712, and/or may correspond to respective ordered temporalsequences of variables 712 during the data-collection time interval.While embodiment 700 illustrates 7 variables 712, in some embodimentsthere may be fewer or more variables 712.

Other intricacies associated with migraines are so-called rebound andrecurrence headaches. While analgesics are designed to relieve pain, ifsuch pharmacological agents (both prescription and nonprescription) areoverused (repetitive and chronic use), they can actually causeheadaches. This is known as analgesic rebound headache (ARH) or “reboundheadache.” Headache sufferers taking analgesic medications every day, oreven as infrequently as two times a week, may find that they must takeever-increasing dosages to achieve relief. With continued overuse themedication becomes less and less effective, with pain-free periodsbetween headaches becoming shorter and shorter. The result can be aself-sustaining cycle of increasing pain and medication.

Recurrences headaches are associated with headaches returning after apain-free period following treatment with one or more medicines. Inessence, the migraine attack “outlasts” the treatment, so the headachereturns when the medication wears off Recurrence is commonly seenfollowing treatment with a triptan. For example, a headache resolveswithin one to two hours after taking a triptan, only to return fullblown (i.e., with full severity) within 24 hours. In some embodiments, arecurrence headache may be defined as any headache occurring after aheadache-free state at 2 hours and within 12 hours after intake of anacute pharmacological agent. In some embodiments, a recurrence headachemay be defined as any headache occurring after a headache-free state at2 hours and within 24 hours after intake of an acute pharmacologicalagent. A recurrence headache may have different characteristics ofintensity, severity and/or associated features than an original headacheepisode during a migraine attack. In some cases, a recurrence headachemay be of migraine or tension-type. (Some characteristics of tensionheadaches are summarized in Table III.)

TABLE III Some characteristics of tension headaches. A. At least 2 ofthe following 4 headache features: 1. Bilateral location; 2.Pressing/tightening quality; 3. Mild or moderate intensity; 4. Notaggravated by routine physical activity. B. Both of the following: 1. Nonausea or vomiting 2. Not more than one of light or sound sensitivity.C. Duration lasting from 30 minutes to 7 days.

In the context of the disclosed embodiments, the one or more events maybe one or more migraines, and the one or more temporal onsetscorresponding to the one or more events may be a respective onset timeand/or a respective onset time interval for one or more migraines. Onsettimes for migraines may be determined in accordance with one or morepremonitory symptoms (discussed further below with reference to FIG. 8A)that may be experienced by at least the first individual during theprodrome phase of a migraine attack, one or more migraine symptomsduring the aura phase of a migraine attack, one or more migrainesymptoms during the headache phase of a migraine attack, and/or an onsetof head pain as indicated by at least the first individual.

In some embodiments, the one or more physiological monitors 372 (FIG. 3)may, at least in part, determine one or more onset times for migraines.Since migraines impact the hypothalamus, with consequences for theendocrine system, the limbic system and the autonomic nervous system, avariety of physiological changes may be observable in one or moremigraine patients. These physiological changes may include changes in acircadian rhythm, changes in one or more vital signs (such as pulse,respiration, systolic blood pressure, and/or diastolic blood pressure),hormonal changes, emotional changes, changes in a pulse pressure(defined as a difference of the systolic blood pressure and thediastolic blood pressure), changes in skin electrical or thermalconductivity (such as perspiration), and/or changes in at least onereflex arc. The physiological changes may be bilateral or unilateral. Inan exemplary embodiment, the pulse pressure may increase or decrease by1%, 3%, 5% or more than 10% during the prodrome phase. In someembodiments, the one or more physiological monitors 372 (FIG. 3) maydetermine a presence of (sub-) cutaneous allodynia or ‘skin pain’ (suchas a sensitive or painful scalp), a condition associated with centralsensitization, which is indicative of a deeply entrench migraine attack.In some embodiments, the one or more physiological monitors 372 (FIG. 3)may provide a metric of chronic disease regulation, for example, afrequency and/or a severity of migraines.

The pre-determined questions in the questionnaire, such as thequestionnaire 600 (FIG. 6A), may correspond to patterns of occurrence(including presence and absence information) of the set of variablesthat are potential migraine triggers. The one or more associationvariables may correspond to one or more migraine triggers, and/or one ormore probable or putative migraine triggers. The one or more associationvariables may be patient-specific, may occur in one or more groups ofmigraine patients, and/or may occur in at least a plurality of migrainepatients. The one or more association variables may at least in partinduce a migraine in at least the first individual if at least the firstindividual is exposed to one or more of the association variables. Insome embodiments, the one or more association variables may be thedominant migraine triggers, such as those migraine triggers associatedwith 10%, 25%, 33%, 50%, or more of the migraine attacks, for at leastthe first individual.

Variables that may be migraine triggers may include weather changes,allergens, compounds containing phenols (also referred to as phenoliccompounds), pollution, hormonal fluctuations (such as during themenstrual cycle, pregnancy, post partum, and/or menopause), trauma,illness, hypoglycemia, sensory stimuli (such as lights, sounds, and/orsmells), physical exertion, sexual activity, motion, travel, sleeppatterns (when and/or how much sleep), intense emotion, withdrawal ofintense emotion, stress, withdrawal of stress, certain pharmacologicalagents (such as MAO inhibitors, oral contraceptives, estrogenreplacement therapy, recreational drugs, and/or tobacco products),dietary patterns (when food is consumed), and/or diet (what and/or howmuch is consumed). Dietary migraine triggers may include alcohol (forexample, wine), sugar substitutes (such as Aspartame), caffeine(including caffeine withdrawal), food additives (such as monosodiumglutamate or MSG), one or more fruits, one or more vegetables, one ormore spices, one or more nuts, fermented food (such as vinegar), foodscontaining amounts of certain amino acids (such as tyramine) that exceedone or more quantity thresholds, foods containing amounts of nitratesthat exceed a first quantity threshold, foods containing amounts ofsulfites that exceed a second quantity threshold, and/or foodscontaining amounts of tannins that exceed a third quantity threshold.For example, dietary migraine triggers may include blue cheese, oranges,carrots, vinegar and caffeine.

Attention is now directed towards application of the embodiments of thequestionnaire for collecting information associated with migraines, suchas variables corresponding to potential or putative migraine triggers.It should be understood, however, that the description applies tonumerous applications and embodiments, including non-medicalapplications.

Prior to or at the beginning of the data-collection time interval, atleast the first individual may answer one or more questions in theoptional initial survey. In some embodiments, the questions may be basedon at least the first individual's medical history. The optional initialsurvey may confirm that at least the first individual meets anyapplicable entry criteria, determine one or more questionnaire modules(discussed further below with reference to FIG. 8A) that may be relevantfor at least the first individual, and collect initial information, suchany pharmacological agents that at least the first individual takes on aregular basis (for example, daily). In the case of migraines, entrycriteria may include determining that the disease in a respectivepatient, such as at least the first individual, is sufficiently wellcontrolled that migraine attacks are not occurring too often (such asevery day) or too infrequently (such as once a year) to precludedetermination of one or more migraine triggers. For migraines,pharmacological agents may include one or more acute therapies and/orone or more preventive therapies. The pharmacological agents may includeother medicines (prescription and/or non-prescription), vitamins, herbs,supplements, and/or recreational drugs that at least the firstindividual takes on a regular basis. The initial information may includequantities and/or times when one or more of the pharmacological agentsare used.

As noted above, pre-determined-questions in the questionnaire may begrouped into questionnaire modules. FIG. 8A is a block diagramillustrating an embodiment of a questionnaire data structure 800including multiple pre-determined questions, such as the pre-determinedquestions 354 (FIG. 3), arranged in multiple questionnaire modules, suchas the questionnaire modules 356 (FIG. 3). The questionnaire datastructure 800 may include sleep pattern questions 810 (includingquestions related to sleep apnea and/or insomnia), dietary questions 812(such as dietary patterns and diet), behavioral questions 814 (such ashormonal fluctuations, physical exertion, sexual activity, motion,travel, exposure to intense emotion, withdrawal of intense emotion,exposure to stress, withdrawal of stress, and/or a use of tobaccoproducts), environmental questions 816 (such as exposure to sensorystimuli, exposure to compounds containing phenols, and/or exposure toweather conditions such as strong wind), overall health questions 818(such as pregnancy, a presence of trauma, illness, depression, and/orhypoglycemia), premonitory questions 820, migraine questions 822,medicine usage questions 824 (such as preventive therapies, vitamins,herbs, oral contraceptives, estrogen replacement therapy, recreationaldrugs, and/or pharmacological agents, including analgesics, other thanmigraine-specific drugs such as triptans), and/or derived variable(s)questions 826.

The premonitory questions 820 include symptoms that may be experiencedand/or exhibited by at least the first individual during the prodromephase of a migraine attack. Premonitory symptoms may include excitatorysymptoms, inhibitory symptoms and/or localized pain (for example, in thehead, neck and/or shoulders). Excitatory symptoms may include cravings(such as hunger and/or thirst), increased activity, sweating, a sense ofwell being, emotional changes (such as irritability), increasedurination or bowel movements, and/or increased sensitivity to sensorystimuli. Inhibitory symptoms may include confusion, difficultyconcentrating, depression, dizziness, fatigue, constricted circulation,yawning, and/or a lack of appetite.

The migraine questions 822 may include migraine occurrence information,migraine information (such as pain location, severity, quality ordescription, patterns, and/or temporal variation), use of medicines(including pharmacological agents such as one or more acute therapies),use of non-pharmacological treatments, a presence of visualdisturbances, symptoms of (sub-) cutaneous allodynia, and/or othermigraines symptoms (such as nausea and/or vomiting). The migraineoccurrence information may include one or more temporal onsets or onsettimes.

In some embodiments, at least the first individual may be asked thederived variable(s) questions 826. One or more answers to the derivedvariable(s) questions 826 may be determined in accordance with one ormore answers to one or more pre-determined questions in one of the otherquestionnaire modules. For example, exposure to a food containingtyramine may be determined based on one or more answers to one or morepre-determined questions in the dietary questions module 812. One ormore answers to the derived variable(s) questions 826 may be determinedin accordance with a mapping operation performed on one or more answersto one or more pre-determined questions in the dietary questions module812. For example, one or more foods consumed (such as mayonnaise) may bemapped to basic constituents (egg, vinegar, and/or mustard) and/orelemental constituents (minerals, fats, carbohydrates, and proteins).The mapping operation may be performed using tables of relatedinformation, such as one or more recipes and/or elemental constituentinformation. Elemental constituent information for some foods may beobtained in the National Nutrient Database on the United StatesDepartment of Agriculture's website atwww.nal.usda.gov/fnic/foodcomp/Data. One or more answers to the derivedvariable(s) questions 826 may be determined in accordance with otherpublic information, such as weather (conditions and/or changes),altitude, allergen, and/or pollution information. For example, pollutioninformation may be obtained from the United States EnvironmentalProtection Agency's website at www.epa.gov/air/data. In someembodiments, one or more answers to the derived variable(s) questions826 may be determined in accordance with at least the first individual'slocation(s), which may determined using the optional location module 370(FIG. 3), during the data-collection time interval.

The pre-determined questions in one or more of the questionnaire modulesmay correspond to deviations from normal or usual behavior for at leastthe first individual. For example, deviations from normal sleep patternsfor at least the first individual, deviations from normal behavior whileat least the first individual is awake, and/or deviations from normaldietary behavior for at least the first individual.

In some embodiments, a respective questionnaire module, such as thedietary questions 812, may include primary questions, such as theprimary questions 610 (FIG. 6A), and secondary questions, such as thesecondary questions 620 (FIG. 6B). For example, the dietary questions812 may include are primary questions such as pre-determined questions828 (“Did you miss a meal today?”), 832 (“Did you have your snack at theusual time?”) and 836 (“Did you eat at a restaurant today?”).Pre-determined questions 830-1 (Did you miss breakfast today?”),pre-determined questions 830-2 (“Did you miss lunch today?”),pre-determined questions 830-3 (“Did you miss dinner today?”),pre-determined questions 834-1 (“Was the snack early or late?”) andpre-determined questions 834-2 (“How [Early or Late] was the snack?”)may be secondary questions. Note that predetermined (secondary) question834-2 may depend on the answer to pre-determined (secondary) question834-1.

At least some of the dietary questions 812 may be displayed using aformat such as that illustrated in FIG. 6A and/or 6C. Other questions inone or more other questionnaire modules may be displayed using a formatsuch as that illustrated in FIG. 6B. For example, medicine usagequestions 824 and/or the use of medicines pre-determined questions inthe migraine questions 822 may be displayed using the format in FIG. 6B.Pre-selected answers 616 (FIG. 6A) for some of the pre-determinedquestions may correspond to a usual or a normal behavior for at leastthe first individual in accordance with at least the first individual'sanswer history 360 (FIG. 3), the answer history 360 (FIG. 3) for one ormore groups (such as men, women, an age group, a demographic group,groups of migraine patients, and/or groups of migraine patients havingone or more migraine triggers in common), one or more answers to theoptional initial survey, and/or one or more default answers 362 (FIG.3).

Answers, be they pre-selected or not, to the pre-determined questions inthe dietary questions 812, as well as in one or more other questionnairemodules, may be categorical, ordered categorical, and/or quantitative.For example, the answer to pre-determined question 828 may be ‘yes’ or‘no’. The answer to pre-determined (secondary) question 834-2 may bequantitative (a time in hours, minutes and/or seconds) and/or orderedcategorical (between 0-1 hours, between 1-2 hours, etc.). Orderedcategorical answers to some of the primary or secondary questions mayinclude time intervals, such as the time intervals 656 (FIG. 6B), thatcorrespond to a fraction of a day. In an exemplary embodiment, apresence or absence of a respective variable may be determined inaccordance with a selection of a ‘yes’ answer to a primary question anda selection of one or more time intervals that correspond to a fractionof a day in answer to a related secondary question. The time intervalsmay include night, morning, afternoon and evening, where night isbetween 12 am and 5.59 am, morning is between 6 am and 11.59 am,afternoon is between 12 pm and 5.59 pm and evening is between 6 pm and11.59 pm.

The questionnaire data structure 800 may include fewer or additionalquestionnaire modules. One or more of the questionnaire modules mayinclude one or more questions corresponding to feedback from at leastthe first individual and/or suggestions for additional variables to betracked (information to be collected) and/or analyzed. Such feedback mayallow a knowledge base to grow and improve as the approach is scaled tomore individuals. Two or more questionnaire modules may be combined.Some pre-determined questions may be included in more than onequestionnaire modules. One or more questionnaire modules may includefewer or more pre-determined questions. In some embodiments, one or morepre-determined questions may be moved from one questionnaire module toanother.

As noted previously, the subset of pre-determined questions may bevaried during the data-collection time interval, i.e., the questionnairemay be used dynamically. The varying may be in accordance with theconfiguration instructions 420 (FIG. 4), with providing one or morepre-determined questions 354 in FIGS. 3 and 4 (for example, in a datastream that is transmitted and stored in the memory device 324 in FIG.3), with providing instructions corresponding to one or morepre-determined questions (such as in instructions corresponding to oneor more web pages 412 in FIG. 4), and/or with providing the optionalmemory device 524 in FIG. 5 containing one or more predeterminedquestions 354.

In some embodiments, an initial phase of the data-collection timeinterval may, at least in part, correspond to a training phase, for atleast the first individual (i.e., how to answer the pre-determinedquestions), for one or more of the apparatuses, and/or for one or morealgorithms implementing the questionnaire (for example, how best todetermine the one or more temporal onsets for at least the firstindividual). During the training phase, the subset of pre-determined mayinitially include one or more pre-determined questions selected from thepremonitory questions 820, the migraine questions 822 and/or themedicine usage questions 824.

The questionnaire, and the related statistical analysis (described belowwith reference to FIGS. 9-14), may be applied iteratively. For example,pre-determined questions in one or more of the questionnaire modules maybe tree-based or hierarchical, ranging from general or broad in scope tonarrow or specific in scope. General pre-determined questions may beasked one or more times during the data-collection time interval. Basedon one or more answers to these general pre-determined questions,additional narrow pre-determined questions may be asked one or moretimes. In some embodiments, the subset of pre-determined questions maybe asked, one or more association variables (for example, migrainetriggers) may be identified and at least the first individual mayexclude one or more of the identified association variables (forexample, by modifying behavior, changing diet, etc.). This process ofasking, identifying and excluding may be repeated one or more timesuntil diminishing returns (for example, it may become difficult toreadily and/or reliably identify one or more additional associationvariables).

FIG. 8B is a block diagram illustrating an embodiment of a questionnaire850 that is dynamic and hierarchical. One or more pre-determinedquestions in question modules are included in the subset ofpre-determined questions as a function of time 852. General sleeppattern questions 854 may be included. Specific sleep pattern questions856 may be included on two occasions. General behavioral questions andspecific migraine questions 858 may be included. Specific migrainequestions 860 may be included. The questionnaire 850 is meant to beillustrative of a dynamic questionnaire and is not indicative of aspecific implementation. Thus, there may additional or fewer portions ofquestion modules, additional or fewer question modules, an order or twoor more of the question modules may be changed, at least a portion oftwo or more the question modules may be combined, and/or at least aportion of one or more additional question modules may be included atany instance in time.

In order to reduce or eliminate inaccuracies associated with memory orrecall errors, in some embodiments at least the first individual may notbe able to answer or modify answers in one or more subsets ofpre-determined questions, such as those in the questionnaire 850, thatwere asked at previous instances in time, for example, on one or moreprevious days.

Attention is now directed towards embodiments of the statisticalanalysis, including the determination of one or more statisticalrelationships between one or more temporal onsets and one or morevariables and/or one or more compound variables, and the identificationof one or more association variables. The statistical analysis mayinclude classification and/or regression (such as determining a model ofthe temporal onsets including one or more variables and/or one or morecompound variables with corresponding weights). FIG. 9A is a blockdiagram illustrating an embodiment 900 of determining a compoundvariable 920 associated with events having different temporal onsets910. In some embodiments, the events may be migraine attacks and thetemporal onsets 910 may be onset times for migraine attacks. Temporalonsets 910 are shown as a function of time 908. The temporal onsets 910may include one or more onset times and/or one or more onsets during oneor more time windows or time intervals. There is a time delay 922between temporal onset 910-1 and temporal onset 910-2. An inverse of thetime delay 922 may correspond to a frequency of the events. Patterns ofoccurrence of variable A 914 and variable D 916, including instances orentries corresponding to presence information (illustrated by arrows)and corresponding to absence information (illustrated by absences ofarrows), as function of time 908 are illustrated on separate butidentical axes for clarity.

The compound variable 920 may correspond to at least a pattern ofoccurrence of variable A 914 during a first time interval 912 precedingthe temporal onsets 910 and a pattern of occurrence of variable D 916during a second time interval 918 preceding the temporal onsets 910.Note that in embodiment 900, the first time interval 912 may be offset924 from the temporal onsets 910. In some embodiments, the first timeinterval 912, the second time interval 918, and/or additional timeintervals corresponding to additional variables may be offset from thetemporal onsets 910. In some embodiments, a pattern of occurrence of atleast one variable may be in accordance with one or more time intervalshaving a width that corresponds to a precision of a time measurement,i.e., each of the one or more time stamps corresponds to a respectivetime.

In some embodiments, the first time interval 912, the second timeinterval 918, and/or other time intervals may have the same durationand/or offsets 924. In some embodiments, the first time interval 912,the second time interval 918, and/or other time intervals may have adifferent duration and/or offsets 924. In some embodiments, one or moreof the time intervals may be adjustable. In exemplary embodiments, thetime intervals may have a duration of a fraction of a day (such as 1, 2,3, 4, 6, 12, and/or 18 hours), one day, two days, three days, more days,and/or combinations of these items. In some embodiments, offsets, suchas offset 924, may be between 0 and up to 3, 5, and/or 10 or more days.In exemplary embodiments, the offset 924 may be a fraction of a day(such as 1, 2, 3, 4, 6, 12, and/or 18 hours), one day, two days, threedays, more days, and/or combinations of these items.

A respective instance or entry for the compound variable 920, such ascompound variable 920-1, may correspond to a presence if variable A ispresent during the first time interval 912-1 (a presence entry in thepattern of occurrence of variable A 914) and variable D is presentduring the second time interval 918-1 (a presence entry in the patternof occurrence of variable D 916). Alternatively, a respective instanceor entry for the compound variable 920, such as compound variable 920-2,may correspond to an absence if variable A is absent during a first timeinterval 912-2 (an absence entry in the pattern of occurrence ofvariable A 914) and/or variable D is absent during a second timeinterval 918-2 (an absence entry in the pattern of occurrence ofvariable D 916).

In an exemplary embodiment, entries for the pattern of occurrence ofvariable A 914 during the first time interval 912 and the pattern ofoccurrence of variable D 916 during the second time interval 918 may becategorical or may be converted from quantitative to categorical bypartitioning using one or more thresholds. In some embodiments,different thresholds may be used for different variables. In someembodiments, one or more compound variables may be a weighted summationof one or more variables. The resulting one or more compound variablesmay be converted into categorical data using one or more thresholdsand/or one or more quantitative variables may be converted intocategorical data using one or more thresholds prior to generating one ormore compound variables using a weighted summation.

Note that entries in the patterns of occurrence for categoricalvariables are typically represented by codes. For categorical variableshaving two class or categories, a single binary digit may be used, suchas 0 or 1, or −1 or 1. When there are more than two categories, such aswith ordered categorical variables, a dummy variable having K values orbits may be used. Entries for the compound variable 920 may determinedby performing an operation and/or a logical operation on correspondingentries in the pattern of occurrence of the variable A 914 and thepattern of occurrence of the variable D 916. The operation may includemultiplication. The logical operation may include a Boolean operation,such as AND. A wide variety of coding approaches, however, may be usedin different embodiments for representing presence and absenceinformation in the pattern of occurrence of variable A 914 and thepattern of occurrence of variable D 916. Therefore, in some embodimentsthe logical operation may include AND, OR, NOT, XOR, as well ascombinations of these operations.

While FIG. 9A illustrates two variables, in some embodiments 3 or morevariables may be used to determine the pattern of occurrence (includingpresence and absence information) for the compound variable 920. While arespective variable has a corresponding time interval and offset (whichmay be zero or finite), in some embodiments at least two variables mayhave time intervals having the same duration and/or the same offset.Similarly, while FIG. 9A illustrates 2 temporal onsets 910, in someembodiments there may be one temporal onset 910 or 3 or more temporalonsets 910, which may be used in determining the pattern of occurrenceof the compound variable 920.

FIG. 9B is a block diagram illustrating an embodiment 950 thatsummarizes the determining of compound variables. Logical operations areperformed on the patterns of occurrence of one or more subsets ofvariables, such as variable A (during time interval I) 960 and variableB (during time interval II) 962, and variable D (during time interval I)964, variable A (during time interval III) 966 and variable B (duringtime interval II) 962.

A number of variables included in determining a respective compoundvariable is henceforth referred to as an order n. FIG. 9B illustratesdetermination of a compound variable of order 2 and a compound variableof order 3. In some embodiments, a respective variable during a timeinterval, such as variable D (during time interval I) 964, may beincluded once in determining a respective compound variable, i.e.,multiple instances of the respective variable during the time intervalmay not be included in determining the respective compound variable.However, the respective variable may be included more than once indetermining the respective compound variable if different time intervalsare used, such as variable A (during time interval I) 960 and variable A(during time interval II) 966. In some embodiments, there may beadditional or fewer variables, i.e., the order may be 1 (a respectivecompound variable is merely a variable) or 4 or more. In someembodiments, time interval I may correspond to a duration of 24 hourswith an offset 924 (FIG. 9A) of zero from the temporal onsets 910 (FIG.9A). Time interval II may correspond to a duration of 24 hours with anoffset 924 (FIG. 9A) of 48 or 72 hours from the temporal offsets 910(FIG. 9A). In some embodiments, there may be additional or fewervariables included in a respective compound variable, and/or there maybe fewer or additional time intervals.

Referring back to FIG. 9A, as discussed further below one or morestatistical relationships between the patterns of occurrence of one ormore compound variable, such as compound variable 920, and/or thepattern of occurrence of one or more of the variables, such as variableA 914, and the temporal onsets 910 may be determined. In the case ofmigraines, however, one or more temporal onsets 910 corresponding to oneor more rebound headaches, one or more recurrence headaches, and/or oneor more tension headaches may be excluded during the determining of theone or more statistical relationships. This may improve the results ofthe statistical analysis. For example, the one or more rebound headachesmay be identified in accordance with a medicine usage history forpharmacological agents, such as analgesics and/or triptans. In someembodiments, the one or more rebound headaches may be identified, atleast in part, if there is no pain-free period between migrainesattacks.

In addition, entries in the pattern of occurrence of one or morevariables that occur during the duration of an event, such as amigraine, may be excluded in determining one or more compound variablesand/or one or more statistical relationships. The reason for thisexclusion operation may be that such entries, corresponding to thepresence of one or more variables, may not trigger an event since anevent is already occurring. Said differently, it may not be possible toinitiate something that is already occurring. FIG. 10 is a block diagramillustrating an embodiment 1000 with a variable D 916-2 occurring duringa duration 1010 of a migraine. As a consequence, the presence ofvariable D 916-2 may be excluded from the determining of compoundvariable 920 (FIG. 9A) and/or one or more statistical relationships,such as those between temporal onsets 910 and the pattern of occurrenceof the compound variable 920 (FIG. 9A) and/or between temporal onsets910 and the pattern of occurrence of variable D 916. Note that thetemporal onset 910-1 is illustrated as occurring during a time interval1014. In some embodiments, the temporal onset 910-1 corresponds to anonset time (i.e., a specific time). In alternate embodiments, theduration 1010 may be defined with respect to a beginning of the timeinterval 1014, a center of the time interval 1014, or the onset timecorresponding to the temporal onset 910-1. Embodiment 1000 alsoillustrates a threshold 1012 that may be used to convert a quantitativevariable into a categorical variable by partitioning. In otherembodiments, one or more thresholds may include one or more geographicdirections. While embodiments 900 (FIG. 9A) and 1000, illustratevariables, such as the variable D 916, occurring in time intervals 912(FIG. 9A), 918 (FIG. 9A) and 1014 preceding corresponding temporalonsets 910, in some embodiments one or more occurrences of one or moreof the variables in one or more time intervals corresponding to one ormore temporal onsets 910, i.e., one or more time intervals containingboth a respective temporal onset and at least a respective variable, maybe included when determining one or more of the statisticalrelationships.

A wide variety of computational techniques may be used to determine theone or more statistical relationships, including one or more parametricanalysis techniques, one or more non-parametric analysis techniques, oneor more supervised learning techniques and/or one or more unsupervisedlearning techniques. In some embodiments, one or more non-parametricanalysis techniques may be used. As noted previously, non-parametricanalysis techniques make few assumptions about an existence of aprobability distribution function, such as a normal distribution,corresponding to a population from which samples or entries areobtained, or regarding independence of the variables and/or the compoundvariables. In general, non-parametric analysis techniques may use rankor naturally occurring frequency information in the data to drawconclusions about the differences between populations.

The one or more non-parametric analysis techniques may performhypothesis testing, i.e., to test a statistical significance of ahypothesis. In particular, the one or more non-parametric analysistechniques may determine if the one or more temporal onsets and the oneor more compound variables and/or one or more variables arestatistically independent (or dependent) in accordance with astatistical significance criterion. One or more variables and/or one ormore compound variables having a statistically significant relationshipwith the temporal onsets may be used to identify one or more associationvariables. In the case of migraines, the one or more associationvariables may be migraine triggers or potential migraine triggers.

In exemplary embodiments, the non-parametric analysis technique mayinclude a chi-square analysis technique, a log-likelihood ratio analysistechnique (also referred to as G-test), and/or a Fisher's exactprobability analysis technique. In addition to their other advantages,these techniques may be well suited to analyzing an underdeterminedproblem (i.e., sparse sampling in a multi-dimensional variable space),in which there may be a plurality of variables and/or compound variablesand a limited number of entries or samples.

The chi-square analysis technique, the log-likelihood ratio analysistechnique, and the Fisher's exact probability analysis technique may bedetermined using a cross-tabulation or contingency tables (sometimesreferred to as bivariate tables). The Fisher's exact probabilityanalysis technique computes the sum of conditional probabilities ofobtaining the observed frequencies in a respective contingency table andthe conditional probabilities of obtaining exactly the same observedfrequencies for any configuration that is more extreme (i.e., having asmaller conditional probability). The chi-square (χ²) may be determinedusing

${\chi^{2} = {\sum\limits_{i}\frac{( {O_{i} - E_{i}} )^{2}}{E_{i}}}},$and the log-likelihood ratio (LLR) using

${{LLR} = {\sum\limits_{i}{O_{i}{\ln( \frac{O_{i}}{E_{i}} )}}}},$where the summation is over the entries in the respective contingencytable, O_(i) is the i-th observed frequency value, and E_(i) is the i-thexpected frequency value. The following example illustrates anembodiment of determining a statistical relationship using thelog-likelihood ratio.

Consider the data in Table IV. The first column contains the number ofentries in the pattern of occurrence where a variable or compoundvariable is present during a time interval, such as the first timeinterval 912 (FIG. 9A), and a temporal onset is present after a timeoffset, such as the time offset 924 (FIG. 9A) (henceforth denoted byX₁₁) plus the number of entries in the pattern or occurrence where thevariable or compound variable is absent during the time interval and atemporal onset is absent after the time offset (henceforth denoted byX₀₀). X₁₁ is sometimes referred to as a true-true and X₀₀ is sometimesreferred to as a false-false. X₁₁ and X₀₀ are henceforth referred to asco-occurrences.

The second column contains the number of entries in the pattern ofoccurrence where the variable or compound variable is present during thetime interval and a temporal onset is absent after the time offset(henceforth denoted by X₁₀) plus the number of entries in the pattern ofoccurrence where the variable or compound variable is absent during thetime interval and a temporal onset is present after the time offset(henceforth denoted by X₀₁). X₁₀ is sometimes referred to as atrue-false and X₀₁ is sometimes referred to as a false-true. X₁₀ and X₀₁are henceforth referred to as cross occurrences.

TABLE IV An embodiment of a contingency table. Number of Co-OccurrencesNumber of Cross Occurrences (X₁₁ + X₀₀) (X₁₀ + X₀₁) 46 11

If the variable or the compound variable and the temporal onsets arecompletely independent, the expected frequency values for each column,E₁ and E₂, would equal 28.5, one half of the sum of the number ofco-occurrences and cross-occurrences, i.e., the total number ofobservations (data points or samples) in Table IV. Therefore, for TableIV

${LLR} = {{{{2 \cdot 46}\;{\ln( \frac{46}{28.5} )}} + {{2 \cdot 11}\;{\ln( \frac{11}{28.5} )}}} = {{44.04 - 20.94} = {23.10.}}}$A one-sided minimal statistical significance confidence criterion of 5%(α=0.05) or statistical confidence threshold based on the number ofdegrees of freedom (the size of the contingency table) corresponds to anLLR of 3.841. Since the LLR for Table IV is greater than 3.841, it isstatistically significant. From a statistical significance perspective,therefore, the temporal onsets and the pattern of occurrence of thevariable or compound variable in this example are dependent. Note thatthe determination of the statistical relationship for the temporalonsets and the variable or the compound variable in this embodiment usespresence and absence information in the pattern of occurrence of thevariable or compound variable. In some embodiments, one or more of thestatistical relationships may be determined using presence information,i.e., the presence of one or more variables or one or more compoundvariables during one or more time intervals, without using absenceinformation. In alternate embodiments, a wide variety of analysistechniques may be used to determine the one or more statisticalrelationships, including one or more non-parametric analysis techniquesand one or more parametric analysis techniques.

In parametric analysis, a Pearson's product-moment correlationcoefficient r may be useful in summarizing a statistical relationship.For some contingency tables, Cramer's phi φ, the square root of χ² orthe LLR divided by the number of observations N, may have a similarinterpretation to r (although, it is known that Cramer's phi φ mayunderestimate r). In the example illustrated in Table IV,

$\varphi = {\sqrt{\frac{LLR}{N}} = {\sqrt{\frac{23.1}{57}} = {0.64.}}}$

The chi-square analysis technique and the log-likelihood ratio analysistechnique may have a maximal sensitivity for contingency tables based onpatterns of occurrence of variables or compound variables having 50%presence entries and 50% absence entries. In addition, in embodimentswhere temporal onsets, such as temporal onsets 910 (FIG. 9A), correspondto onsets during one or more time windows or time intervals, maximalsensitivity may occur if 50% of these time windows or time intervalshave a temporal onset (i.e., a presence entry). In some embodiments, oneor more contingency tables may be generated to achieve approximately 50%presence entries for patterns of occurrence of one or more variables orone or more compound variables, and/or 50% temporal onsets by using asubset of the collected information or data. In an exemplary embodiment,one or more contingency tables may be generated by approximatelyrandomly (including the use of a pseudo-random number generator oralgorithm) selecting a subset of the temporal onsets, and/orapproximately randomly selecting a subset of the presence or absenceentries of one or more patterns of occurrence of one or more variablesor one or more compound variables such that the one or more contingencytables may have approximately 50% presence entries and 50% absenceentries distributed over X₀₀, X₁₁, X₁₀, and X₀₁. For infrequentlyoccurring events, variables, and/or compound variables, there may bemore absence entries than presence entries in the collected data orinformation. As a consequence, different sampling ratios may be used forpresence and absence entries.

In some embodiments, boosting may be used when generating one or morecontingency tables. The fraction of the collected information may beapproximately randomly sampled to generate one or more contingencytables. A respective contingency table may be generated N times usingapproximate random sampling. Statistical relationships for at least M ofthese N contingency tables may be used (including combining and/oraveraging) to determine whether or not the temporal onsets and thecorresponding variable or compound variable are independent. In anexemplary embodiment, N may be 5, 10, 25, 50, 100 or more. M may be 50%(rounded to the nearest integer), 60%, 66%, 70%, 75%, 80% or more of N.

In some embodiments, there may be too few presence entries or too manypresence entries in one or more patterns of occurrence of one or morevariables or compound variables to reliably determine statisticallysignificant independence (or dependence) from the temporal onsets. As aconsequence one or more of these variables or one or more of thesecompound variables may be excluded when determining one or morestatistical relationships. In an exemplary embodiment, one or morevariables or one or more compound variables having patterns ofoccurrence with less than 10% presence entries or more than 85% presenceentries may be excluded. To assist in obtaining sufficient presence andabsence entries, in some embodiments at least the first individual maybe instructed to vary their activities and/or diet from day to dayduring the data collection time interval.

Overfitting is a risk when developing a model in a statistical leaningproblem. In some embodiments, this risk may be addressed by using afraction or percentage of the collected data or information (patterns ofoccurrence and temporal onsets) for training, i.e., to develop themodel, and a remainder for testing the resulting model. This isillustrated in FIG. 11, which is a block diagram illustrating anembodiment 1100 of determining model complexity. In some embodiments,the model complexity may correspond to a number of variables or compoundvariables that have statistically significant dependence on the temporalonsets. In some embodiments, the model complexity may, at least in part,correspond to a number of variables included in a respective compoundvariable, i.e. the order n. Embodiment 1100 shows a magnitude of atraining and/or a test error 1112 as a function of model complexity1110. A training error 1114 typically decreases as the model complexity1110 increases (the model better fits or predicts a training set ofdata). A test error 1116 typically exhibits a minimum. Additional modelcomplexity 1110 beyond this point does not generalize well (the modeloffers a poorer fit or prediction for a test set of data). Beyond thispoint, therefore, the training set of data may be overfit 1118. In anexemplary embodiment, the percentage of the collected information usedfor training may be 70%, 75%, 80%, 85% or 90%.

An additional metric of the model complexity may be determined. Thismetric may be used in conjunction with or independently of the trainingset of data and the test set of data. The additional metric is describedbelow. In some problems and/or embodiments, determining one or morestatistical relationships for one or more variables (or, saiddifferently, one or more compound variables of order 1) may not besufficient to determine statistically significant independence (ordependence) with respect to the temporal onsets. For example, inmulti-dimensional problems, where exposure to two or more variables inat least close temporal proximity may be necessary to initiate atemporal onset (such as a migraine), a value of the Fisher's exactprobability, χ², and/or LLR for a compound variable of order 1 may bereduced since there is a penalty for the presence of the crossoccurrences, X₁₀ and X₀₁.

More generally, the value of the Fisher's exact probability, χ², and/orLLR may be reduced if the order n of one or more compound variables isless than an intrinsic order of the multi-dimensional problem. In thecase of X₁₀, a temporal onset may or may not occur unless a certainnumber of variables or a set of variables (which may be inter-operative)are present in close temporal proximity. And in the case of X₀₁, morethan one set of variables may be present, i.e., one or more variables inanother set of variables may have triggered the corresponding temporalonsets. As illustrated in FIG. 7, in the embodiments for migraines theremay also be variations in a patient's sensitivity threshold to avariable or one or more sets of variables as a function of time.

To assess whether or not the model has sufficient complexity, i.e.,whether or not one or more compound variables have been determined tosufficient order n, a ratio R may be determined. R is defined as X₁₁divided by the total number of occurrences of the variable or compoundvariable of order n, i.e.,

$R = {\frac{X_{11}}{( {X_{11} + X_{10}} )}.}$An increasing value of R, and/or Cramer's phi φ, as statistical analysisis performed to higher order (i.e., n+1) may be metrics of goodness,i.e., it may indicate that the higher order does a better jobdetermining statistically significant independence or dependence betweenone or more compound variables and the temporal onsets. In someembodiments, contingency tables for one or more compound variables maybe generated for progressively higher orders. Once the ratio R is closeto or equal to one, i.e., X₁₀ is close to or equal to zero, furtherincreases in the order of one or more compound variables may not beneeded, i.e., the model has sufficient complexity.

One or more variables and/or compound variables having statisticallysignificant statistical relationships with the temporal onsets may beidentified as one or more association variables. For a respectivecompound variable or order n having a significant statisticalrelationships with the temporal onsets, the n constituent variables maybe identified as n association variables and/or as a set of associationvariables. In some embodiments, one or more statistically significantcompound variables of order n having the ratio R approximately equal to1 may be identified as one or more association variables. In theembodiments for migraines, one or more association variables may be oneor more migraine triggers or one or more probable migraine triggers.

In some embodiments, one or more compound variables of order n and/orone or more constituent variables in the one or more compound variablesof order n may be ranked in accordance with the corresponding determinedstatistical relationships that are statistically significant. In someembodiments, a ranking of a respective constituent variable is inaccordance with a number of occurrences of the respective constituentvariable in one or more compound variables of order n having statisticalrelationships that are statistically significant. Ranking may beperformed as the statistical significance confidence criterion (a) isprogressively increased.

In exemplary embodiments, α may be 0.05 or lower. For a respectiveranking, a pareto corresponding to at least a subset of the respectiveranking may be defined. The pareto may correspond to variables orcompound variables having a statistical relationship or a number ofoccurrences exceeding a threshold. In some embodiments, a top 10, 20, 50or 100 variables or compound variables may be used, or a plurality ofthe top 10, 20, 50 or 100 variables or compound variables may be used.For compound variables of order n, approximate stability of the paretoas the statistical significance confidence criterion is increased may beused to identify a noise floor. Approximately stability may include anapproximately unchanged order n in the ranking or a presence ofapproximately the same variables (for example, more than 70%) in theranking. In exemplary embodiments, the noise floor may correspond to anα of 0.01 or lower, an α of 0.001 or lower, or an α of 0.0001 or lower.One or more variables and/or one or more compound variables in paretoscorresponding to one or more statistical significance confidencecriteria that exceed the noise floor may be identified as associationvariables.

In some embodiments, one or more variables and/or one or more compoundvariables in paretos corresponding to one or more statisticalsignificance confidence criteria that exceed the noise floor may be usedas a seed set in a subsequent statistical analysis. The subsequentstatistical analysis may determine statistical relationships forcompound variables of a higher order. In some embodiments, thesubsequent analysis may utilize an analysis technique such as SVM orCART. These and other analysis techniques are discussed further below.

FIG. 12 is a block diagram illustrating an embodiment of rankingvariables 1200. Statistical relationship value 1212 is plotted as afunction of statistical significance 1210, such as the statisticalsignificance confidence criteria. Several rankings 1222 are illustrated.Ranking 1222-1, including variable F (during time interval IV) 1214 andvariable M (during time interval II) 1216, is below a noise floor 1218.Ranking 1222-2 and ranking 1222-3 are above the noise floor 1218. Asubset 1220 of ranking 1222-2 and ranking 1222-3 is stable. The subset1220 may identified as the pareto.

In an exemplary embodiment for migraines, the noise floor 1218corresponds to an α of approximately 0.001. At least 8 of the top-10variables in paretos for more stringent statistical significanceconfidence criteria than that corresponding to the noise floor 1218 arepresent even when an approximately random subset corresponding to 80% ofthe patterns of occurrence and the temporal onset data is used.Excluding probable recurrence headaches, rebound headaches and tensionheadaches increases the statistical relationship values 1212 forcompound variables having an order n corresponding to the pareto.Compound variables of at least order 4 have ratio R values approximatelyequal to 1.

Having identified one or more association variables for at least thefirst individual, one or more additional association variables mayidentified. For example, if one or more groups of association variableshave been previously determined for one or more other individuals, theone or more association variables identified for at least the firstindividual may be used to associate at least the first individual withone or more of these groups. In this way, one or more of the associationvariables in one or more of the groups may be identified as additionalassociation variables for at least the first individual. For example,the one or more additional association variables may be groups ofmigraine triggers and at least the first individual may be associated(classified) with one or more of these groups in accordance with one ormore identified migraine triggers for at least the first individual.

Alternatively, additional association variables may be identified byassociating the identified one or more association variables for atleast the first individual with previously determined groups ofvariables. For example, the identified one or more association variablesfor at least the first individual may be foods and additionalassociation variables may be identified by associating the foods withcorresponding food groups, such as pineapple, mushroom, melon, cashew,banana, or citrus, or groups determined based on an amount ofconstituent elements (minerals, fats, carbohydrates, and/or proteins) infoods. For example, if an identified association variable is in the beetfamily or the citrus family, other members of the beet or citrusfamilies may be identified as association variables.

FIG. 13 is a block diagram illustrating an embodiment 1300 ofassociating one or more variables with one or more groups of variables.Group I 1310 may include variable A 1314, variable F 1316, associatedvariable S 1318, and variable C 1320. Group II 1312 may include variableC 1320 and a compound variable 1322, including variable A (during timeinterval III) plus variable D (during time interval I). If a variable,such as variable A 1314, is determined or identified, one or more of theother variables in group I 1310 may be identified. In some embodiments,there may be additional groups, there may or may not be overlap (such asvariable C 1320) between two or more of the groups, and/or a respectivegroup may include fewer or more variables, fewer or more associatedvariables, and/or fewer or more compound variables.

Having identified one or more association variables in accordance withone or more statistical relationships, rankings, and/or associatedgroups of associated variables, one or more recommendations and/or oneor more reports may be provided to at least the second individual and/orat least the first individual. In an exemplary embodiment, the one ormore recommendations may include a listing of one or more migrainetriggers and/or one or more probable migraine triggers for at least thefirst individual. The one or more recommendations may include one ormore variables for which the statistical analysis was unable todetermine a statistically significant relationship. In some embodiments,the one or more recommendations may indicate one or more migrainetriggers and/or one or more probable migraine triggers that at least thefirst individual may wish to modify (such as for behaviors) and/oravoid. The one or more recommendations may indicate additional analysisthat may be advisable in accordance with one or more of the statisticalrelationships and/or the one or more association variables. One or morecorresponding reports may include the one or more recommendations. Theone or more reports may include a summary for at least the firstindividual. The summary may include a health overview for at least thefirst individual during at least a portion of the data-collection timeinterval. In the case of migraines, the health overview may include asummary of migraine frequency, migraine severity and/or the use of oneor more pharmacological agents, such as one or more acute therapiesand/or one or more preventive therapies.

In some embodiments, the one or more recommendations may include placeboinformation, for example, placebo migraine triggers. After this placeboinformation is provided to at least the first individual (possibly viaan intermediary such as at least the second individual), an impact on atleast the first individual may be determined. For example, migrainefrequency, migraine severity, and/or use of pharmacological agentsduring a subsequent time interval may be determined. An efficacy of theidentified association variables may be determined by comparing thesemetrics with those that occur when non-placebo information is used,i.e., when actual association variables are provided to at least thefirst individual. The difference of these two metrics can be used todefine a therapeutic gain. In some embodiments, the therapeutic gain maybe determined by averaging results for two or more individuals such asat least the first individual.

Attention is now given to other techniques of performing statisticalanalysis, such as determining the one or more statistical relationships.As discussed previously, one or more variables or one or more compoundvariables determined during the statistical analysis, for example, inone or more paretos, may be used in subsequent analysis. In someembodiments, the subsequent analysis may utilize a non-parametricanalysis technique as an initial or first stage. In other embodiments,the subsequent analysis may not utilize a non-parametric analysistechnique. The subsequent analysis may be used as the initial or firststage, to refine the model (including adding or removing one or morevariables and/or one or more compound variables), and/or identify one ormore association variables. The subsequent analysis may includeclassification and/or regression (such as determining a model of thetemporal onsets including one or more variables and/or one or morecompound variables with corresponding weights). As with the initialstatistical analysis, a wide variety of techniques may be used in thesubsequent analysis. Two such techniques, SVM and CART, are describedfurther below.

Embodiments of SVM are instances of supervised learning techniques thatmay be applied to classification and regression problems. For binaryclassification, a set of binary labeled data points (training data orexamples) is provided. SVMs may be used to determine an optimalseparation boundary, defined by the variables and/or compound variables,between two classes of data points. A separation boundary is optimal ifusing it as a decision rule to classify future data points minimizes anexpected classification error. For linearly separable data sets (i.e., aclass of absences, which may be indicated by −1, and a class ofpresences, which may be indicated by +1, may be separated by a line in 2dimensions, or a so-called hyperplane in higher dimensions), SVMs may beused to determine a maximal margin hyperplane. For the maximal marginhyperplane, a linear decision boundary may be positioned such that itseparates both classes and such that the distance to the closest pointfrom each class is maximized. For non-linearly separable data sets, sometraining data points may be allowed on the opposite or “wrong” side ofthe hyperplane, i.e., a classification error on the training data setmay be allowed and may be minimized, while the margin, measured betweenpoints on the “correct” side of the hyperplane, is maximized.

If a linear decision boundary is not sufficiently complicated to modelthe separation between classes accurately, the corresponding linearmodel may be transformed into a non-linear model by non-linearlytransforming the variables and/or compound variables into a possiblyhigher dimensional Euclidean space. A linear decision boundaryconstructed in such a higher dimensional Euclidean space may correspondto a non-linear decision boundary in the original space of variablesand/or compound variables. This approach is referred to as kernel SVM.

Depending on how the margin and training error are measured, and how atrade-off between maximizing the margin and minimizing the trainingerror is established, different types of SVMs may be obtained. In someembodiments, SVM may include standard 1-norm SVM (measuring the marginusing Euclidean distance, i.e., a L₂-norm, and the training error usinga L₁-norm), standard 2-norm SVM (measuring the margin using Euclideandistance, i.e., the L₂-norm, and the training error using the L₁-norm),and/or LP-SVM (measuring the margin using the L₁-norm and the trainingerror using the L₁-norm). Each of these 3 types of SVM may be a C-typeor nu-type SVM. These two varieties correspond to different ways oftrading-off maximizing the margin against minimizing the training error.The 1-norm SVM, standard 2-norm SVM, and/or LP-SVM may be a C+/C− ornu+/nu− type (when errors on positive (+1) labeled training data areweighted differently than errors on negative (−1) labeled trainingdata).

The principle for binary classification described above may be extendedto regression, for example, by copying the regression data twice,shifting both copies in opposite directions (over a distance epsilon)with respect to the continuous output dimension or variable andestablishing a regression surface as a decision boundary between the twoshifted copies that may be regarded as two classes for binaryclassification. As a consequence, in some embodiments, regressionversions of SVMs corresponding to previously described SVMs may be used.

The decision boundary determined using one or more SVMs may be used todiscriminate between temporal onsets and non-temporal onsets. For binaryclassification, measures of goodness for the resulting model include aprediction accuracy that is better than predicting 50% of the positivedata (i.e., occurrences, which may be indicated by a +1) as positive(i.e., true positive predictions) and better than predicting 50% of thenegative data (i.e., absences, which may be indicated by a −1) asnegative (i.e., true negative predictions). Doing better than 50/50corresponds to doing better than random. In an exemplary embodiment, theresulting model successfully predicts at least 80-85% of the true-false(X₁₀) and false-false events (X₀₀), i.e., the true negatives, whilepredicting significantly more than 50% of the true positives correctly,i.e., false-true (false-true (X₀₁) and true-true events (X₁₁)).

CART is non-parametric multivariate analysis technique. It involves thedetermination of a binary decision tree using the training set of data.Predictions based on the resulting tree may be compared to the test setof data (cross validation). A decision tree provides a hierarchicalrepresentation of the feature space in which explanatory variables areallocated to classes (such as temporal onsets or non-temporal onsets)according to the result obtained by following decisions made at asequence of nodes at which branches of the tree diverge. Branches ordivisions of the tree may be chosen to provide the greatest reduction inthe entropy of the variables (for a classification tree based oncategorical data), such as a small or zero standard deviation, or thegreatest reduction in the deviation between the variables (and/orcompound variables) and one or more variables being fit (for aregression tree based on quantitative data). A tree stops growing whenno significant additional reduction can be obtained by division. A nodethat is not further sub-divided is a terminal node. It is associatedwith a class. A desirable decision tree is one having a relatively smallnumber of branches, a relatively small number of intermediate nodes fromwhich these branches diverge, terminal nodes with a non-zero number ofentries, and high prediction power (correct classifications at theterminal nodes). In some embodiments, CART may be used in conjunctionwith a gradient boosting algorithm, where each boosted tree is combinedwith its mates using a weighted voting scheme. Gradient boosting may beused to force the binary decision tree to classify data that waspreviously misclassified.

As noted above, a wide variety of statistical analysis techniques may beused to determine the one or more statistical relationships. These mayinclude one or more supervised leaning techniques, one or moreunsupervised learning techniques, one or more parametric analysistechniques (such as a Pearson's product-moment correlation coefficient ror an inner product), and/or one or more non-parametric analysistechniques. Non-parametric analysis techniques may include a Wilcoxonmatched pairs signed-rank test (for ordinal or ranked data), aKolmagorov-Smirnov one-sample test (for ordinal or ranked data), adependent t-test (for interval or ratio data), a Pearson chi-square, achi-square test with a continuity correction (such as Yate'schi-square), a Mantel Heanszel chi-square test, a linear-by-linearassociation test, a maximum likelihood test, a risk ratio, an oddsratio, a log odds ratio, a Yule Q, a Yule Y, a phi-square, a Kappameasure of agreement, a McNemar change test, a Mann Whitney U-test, aSpearman's rank order correlation coefficient, a Kendall's rankcorrelation, a Krushcal-Wallis One-Way Analysis of Variance, and aTurkey's quick test.

Supervised learning techniques may include least-squares regression(including correlation), ridge regression, partial least-squares (alsoreferred to as partial correlation), a perceptron algorithm, a winnowalgorithm, linear discriminant analysis (LDA), Fisher discriminantanalysis (FDA), logistic regression (LR), a Parzen windows classifier, a(k-) nearest-neighbor classification, multivariate adaptive regressionsplines (MARS), multiple additive regression trees (MART), SVM, LASSO (aregularized linear regression technique like ridge regression, but withL₁-norm regularization of the coefficients), least angle regression(LARS), decision trees (such as CART, with and without gradientboosting, such as ID3 and C4.5), bagging, boosting (such as, adaboost)of simple classifiers, kernel density classification, a minimaxprobability machine (MPM), multi-class classification, multi-labelclassification, a Gaussian Process classification and regression,Bayesian statistical analysis, a Naive Bayes classifier, and neuralnetworks for regression and classification. While some of thesesupervised learning algorithms are linear, it should be understood thatone or more additional non-linear versions may be derived using the same“kernel-methodology”, as previously described for the SVM, leading to aspectrum of kernel-based learning methods, for example, kernel FDA,kernelized logistic regression, the kernelized perceptron algorithm,etc. One or more of these non-linear versions may be used to perform thestatistical analysis.

Unsupervised learning techniques may include a kernel density estimation(using, for example, Parzen windows or k-nearest neighbors), moregeneral density estimation techniques, quantile estimation, clustering,spectral clustering, k-means clustering, Gaussian mixture models, analgorithm using hierarchical clustering, dimensionality reduction, suchas principal component analysis or PCA, multi-dimensional scaling (MDS),isomap, local linear embedding (LLE), self-organizing maps (SOM),novelty detection (also referred to as single-class classification, suchas single-class SVM or single-class MPM), canonical correlation analysis(CCA), independent component analysis (ICA), factor analysis, and/ornon-parametric Bayesian techniques like Dirichlet processes. As notedabove for the supervised learning techniques, one or more additionalnon-linear versions of one or more linear unsupervised learningtechniques may be used to perform the statistical analysis, such askernel PCA, kernel CCA and/or kernel ICA.

In some embodiments, at least a portion of the statistical analysis,such as determination of one or more statistical relationships and/oridentification of one or more association variables may include spectralanalysis. For example, a Fourier transform or a discrete Fouriertransform may be performed on the temporal onsets, one or more patternsof occurrence of one or more variables, and/or one or more patterns ofoccurrence of one or more compound variables. Analysis in the frequencydomain may allow patterns in at least some of the data, such an impactof a woman's menstrual cycle, to be determined.

In some embodiments, determination of one or more statisticalrelationships and/or identification of one or more association variablesmay include the use of design of experiments.

In some embodiments, at least a portion of the statistical analysisand/or identification of one or more association variables may beimplemented using one or more filters, including analog filters, digitalfilters, adaptive filters (using, for example, a least square error orgradient approach, such as steepest decent), and/or neural networks. Theone or more filters may be implemented using one or more DSPs. In someembodiments, the statistical analysis and/or identification of one ormore association variables may be implemented in hardware, for example,using one or more ASICs, and/or software.

FIG. 14 is a block diagram illustrating an embodiment of a signalprocessing circuit 1400 for determining one or more statisticalrelationships and/or identifying one or more association variables.Presence (coded with 1s) and absence information (coded with −1s) forone or more variables 1410 are selectively coupled using selectioncircuit 1416 to one or more filters H_(i) 1418. The selection circuit1416 may be a multiplexer. The filters H_(i) 1418 may perform spectralmodification, such as limiting one or more of the variables 1410 to oneor more time intervals, or one or more sequences of time intervals. Thefilters H_(i) 1418 may convert the presence and absence information forone or more-of the variables 1410 into one or more patterns ofoccurrence.

The filters H_(i) 1418 may be adaptive. The adaptation may be inaccordance with temporal onsets 1412 and/or an error 1426. Theadaptation may include one or more time intervals, such as the firsttime intervals 912 (FIG. 9A), and/or one or more offsets, such as theoffset 924 (FIG. 9A). In some embodiments, the adaptation may minimizeor reduce the error 1426 or a portion of the error 1426. In theembodiments for migraine, for example, the adaptation may reduce apredicted number of migraines, a predicted severity and/or a predictedfrequency.

Outputs from one or more of the filters H_(i) 1418 may be coupled tofilter H_(B) 1420. The filter H_(B) 1420 may perform additional spectralmodification. As a consequence, an arbitrary filtering operation may beimplemented using one or more of the filters H_(i) 1418 and/or thefilter H_(B) 1420. The filter H_(B) 1420 may determine a pattern ofoccurrence for one or more variables 1410 and/or one or more compoundvariables.

The temporal onsets 1412 may be filtered using filter H₃ 1418-3.Comparisons between an output of filter H₃ 1418-3 and an output of thefilter H_(B) 1420 may be performed using statistical analysis element1424. In some embodiments, the statistical analysis element 1424 may bea comparator. Statistical analysis element may implement one or morestatistical analysis techniques, such as the log likelihood ratio. Thestatistical analysis element 1424 may generate the error 1426. The error1426 may be a scalar, a vector, or a matrix. In some embodiments, thestatistical analysis element 1424 may perform a relative time shiftingof the output of filter H₃ 1418-3 and the output of the filter H_(B)1420. In an exemplary embodiment, the statistical analysis element 1424may determine one or more statistical relationships between the temporalonsets 1412 and one or more patterns of occurrence of one or morevariables and/or one or more compound variables. The one or morestatistical relationships may be determined sequentially and/orsubstantially concurrently. The error 1426 may correspond to the one ormore statistical relationships.

In some embodiments, one or more optional additional inputs, such asoptional additional input 1414, may be filtered using one or morefilters, such as filter H₄ 1418, and/or combined with the temporalonsets 1412 using a filter, such as filter/combiner H₅ 1422. An outputfrom the filter/combiner H₅ 1422 may be included in the analysisperformed by the statistical analysis element 1424. The one or moreoptional additional inputs may allow inclusion of cross-terms. In someembodiments, the one or more optional additional inputs may includeother disease symptoms and/or disease conditions.

While a single output is shown for the filter H_(B) 1420, there may beadditional outputs that are used by the statistical analysis element1424. Similarly, there may be additional outputs from thefilter/combiner H₅ 1422 that are used by the statistical analysiselement 1424. While embodiment 1400 uses presence and absenceinformation in the one or more variables 1410, the temporal onsets 1412,and the optional additional input 1414, in some embodiments one or moreof these items may only use presence information. Embodiment 1400 mayinclude fewer elements or additional elements. A position of at leasttwo elements may be switched. Functions of two or more elements may becombined into a single element.

Attention is now directed to embodiments of processes for implementingthe collection of information during the data-collection time interval,the determining of one or more statistical relationships, theidentification of one or more association variables, and/or theproviding of recommendations to at least the first individual and/or atleast the second individual. FIG. 15 is a flow diagram illustrating anembodiment 1500 of a process for collecting information. A deviceincluding a set of pre-determined questions may be optionally provided(1510). Configuration instructions may be optionally received (1512). Asubset of pre-determined questions may be asked, one or more timesduring a time interval, in accordance with the configurationinstructions (1514). Answers may be optionally pre-selected inaccordance with an answer history and/or default answers (1516). Answersto the subset of pre-determined questions may be received one or moretimes during the time interval (1518). Answers to the subset ofpre-determined questions may be transmitted one or more times during thetime interval (1520). Operations in embodiment 1500 may be optionallyrepeated, one or more times (1522). The process in embodiment 1500 mayinclude fewer operations or additional operations. A position of atleast two operations may be switched. Two or more operations may becombined into a single operation.

FIG. 16 is a flow diagram illustrating an embodiment 1600 of a processfor determining one or more association variables. Presence or absenceof one or more variables may be optionally determined in accordance withone or more thresholds (1610). A subset of temporal onsets may beoptionally identified (1612). Pattern(s) of occurrence of one or morecompound variables may be determined (1614). Statistical relationship(s)between temporal onsets or the subset of temporal onsets and thepattern(s) of occurrence may be determined (1616). The compoundvariable(s) may be optionally ranked in accordance with the statisticalrelationship(s) (1618). The variables may be optionally ranked inaccordance with a number of occurrences of the variables instatistically significant statistical relationships (1620). One or moreassociation variables or sets of association variables may be identified(1622). One or more additional association variables may be optionallydetermined or identified in accordance with the one or more associationvariables (1624). Operations in embodiment 1600 may be optionallyrepeated one or more times (1626). The process in embodiment 1600 mayinclude fewer operations or additional operations. A position of atleast two operations may be switched. Two or more operations may becombined into a single operation.

FIG. 17 is a flow diagram illustrating an embodiment 1700 of a processfor providing recommendation(s) and/or report(s). Temporal onsets andpattern(s) of occurrence of one or more variables may be transmitted(1714) from a client computer 1710 to a server computer 1712. Thetemporal onsets and the pattern(s) of occurrence of one or morevariables may be received (1716). One or more statistical relationshipsmay be determined (1718). One or more recommendation(s) and/or report(s)may be transmitted (1720) from the server 1712 to the client computer1710. The one or more recommendation(s) and/or report(s) may be received(1722). The one or more recommendations and/or report(s) may bepresented (1724). The process in embodiment 1700 may include feweroperations or additional operations. A position of at least twooperations may be switched. Two or more operations may be combined intoa single operation.

FIG. 18 is a flow diagram illustrating an embodiment 1800 of a processfor providing one or more reports. A request for a report may beoptionally transmitted (1814) from a client computer 1810 to a servercomputer 1812. The request for the report may be optionally received(1816). One or more reports may be generated (1818). The one or morereports may be transmitted (1820) from the server 1812 to the clientcomputer 1810. The one or more reports may be received (1822). The oneor more reports may be presented (1824). The process in embodiment 1800may include fewer operations or additional operations. A position of atleast two operations may be switched. Two or more operations may becombined into a single operation.

Attention is now directed to embodiments of data structures that may beused in implementing the collection of information during thedata-collection time interval, the determining of one or morestatistical relationships, the identification of one or more associationvariables, and/or the providing of one or more recommendations and/orone or more reports to at least the first individual and/or at least thesecond individual. FIG. 19 is a block diagram illustrating an embodimentof a questionnaire data structure 1900. The questionnaire data structure1900 may include one or more modules 1910. A respective module, such asmodule 1910-1, may include entries for one or more questions 1912, oneor more classifications 1914 for the questions 1912 (such as primary orsecondary, or general or specific), one or more default answers 1916,and/or one or more answer histories 1918. The questionnaire datastructure 1900 may include fewer or addition modules and/or entries. Aposition of two modules and/or a position of two entries may beswitched. Two or more modules may be combined into a single module. Twoor more entries may be combined into a single entry.

FIG. 20 is a block diagram illustrating an embodiment of a datastructure 2000. The data structure 2000 may include one or more sets ofcategories. A respective set of categories may correspond to at leastthe first individual. The respective set of categories may includeidentification 2010 for at least the first individual, meta data 2012(such as relevant demographic, billing and/or medical history data forat least the first individual), configuration instructions 2014,temporal onsets 2016, variable(s) 2022, derived variable(s) 2024,compound variable(s) 2026, statistical relationships 2028, optionalrankings 2030, association variable(s) 2032, group(s) of associationvariables 2034 and/or recommendations/reports 2036. The temporal onsets2016, the variable(s) 2022, the derived variable(s) 2024, and/or thecompound variable(s) 2026 may include one or more entries including timeintervals 2018 and corresponding presence and/or absence information2020. The data structure 2000 may include fewer or addition categoriesand/or entries. Two or more categories may be combined into a singlecategory. A position of two categories and/or a position of two entriesmay be switched. Two or more entries may be combined into a singleentry.

Attention is now directed towards alternative applications for theprocesses and apparatuses for collection of information, determining oneor more statistical relationships, identifying one or more associationvariables, and providing one or more recommendations and/or one or morereports. In some embodiments, one or more fees may be charged foroffering the service of collecting the information and/or identifyingone or more association variables (such as one or more migrainetriggers) for at least the first individual. In some embodiments, theone or more fees may be in accordance with a cost savings associatedwith a reduced usage of one or more pharmacological agents (such as oneor more acute and/or preventive therapies). The one or more fees may becollected from at least the first individual, at least the secondindividual, and/or one or more insurance providers. In some embodiments,information associated with the one or more identified associationvariables may be sold to third parties. In some embodiments, advertisingmay be presented to at least the first individual and/or at least thesecond individual during the collection of information, the providing ofone or more recommendations and/or the providing of one or more reports.Fees may be charged to advertisers for such services.

In some embodiments, at least the first individual may be associatedwith one or more groups, such as one or more groups of migrainepatients, in accordance with one or more identified associationvariables (such as migraine triggers). A respective group may beanalyzed to determine one or more existing or new acute and/orpreventive therapies that may provide improved efficacy for therespective group. Using migraine as an exemplary embodiment, improvedefficacy may include a reduction in migraine frequency, a reduction inmigraine severity, a reduction in recurrence, a reduction in one or moreadverse reactions or side effects, a reduction in the use of one or morepharmacological agents, and/or an improved efficacy in aborting one ormore migraine attacks relative to other acute and/or preventivetherapies.

In some embodiments, association with one or more groups and/or analysisof the respective group may include statistical analysis and/ordetermining a presence or an absence of one or more biological markers,including genetic material, deoxyribonucleic acid, ribonucleic acid, oneor more genes, one or more proteins, and/or one or more enzymes that maybe common to the respective group and/or two or more groups. The one ormore biological markers may be determined by testing one or morebiological samples, including a blood sample, a urine sample, a stoolsample, a saliva sample, a sweat sample, a mucus sample, a skinscrapping, and/or a tear. The one or more biological samples may beanalyzed using chemical analysis, genetic analysis (such as geneticsequencing), nuclear quadrapole resonance, nuclear magnetic resonance,and/or electron spin resonance. In some embodiments, one or morepatients that have been diagnosed with a respective disease, such asmigraine, may be tested for the one or more biological markers toassociate the one or more patients with one or more of the groups ofpatients, and to recommend one or more pharmacological agents (such asone or more acute pharmacological agents, for example, a respectivefamily of triptans, and/or one or more preventive therapies) that mayoffer improved efficacy relative to other pharmacological agents for theone or more patients. Such a test or tests, based on the one or morebiological markers, may reduce or eliminate the current approach oftrial and error in searching for one or more pharmacological agents forpatients, such as one or more effective acute and/or preventivetherapies, which results in delays in patient treatment and additionalexpense.

In some embodiments, the information collected during thedata-collection time interval may be analyzed to determine one or moresubgroups within a population of patients, such as the group of migrainepatients mentioned above. The one or more subgroups may be determinedbased on the one or more identified association variables (such asmigraine triggers), an efficacy of one or more pharmacological agents(such as one or more acute and/or preventive therapies), side effects oradverse reactions to one or more pharmacological agents, and/or patientsymptoms (such as migraine severity and/or frequency). The subgroups maybe determined using statistical analysis and/or determining a presenceor an absence of the one or more biological markers. In someembodiments, the one or more subgroups may be used to study druginteractions in a real-world setting and patient population. In someembodiments, the one or more subgroups may be indicative of underlyingpolymorphism in a genetic basis for a respective disease. Informationcorresponding to the one or more subgroups may be sold to a third party,for example, for use in molecular biology studies of the respectivedisease, the development of one or more pharmacological agents, and/or amanagement of costs associated with the disease.

In an exemplary embodiment, a genetic polymorphism for migraine may bedetermined. Migraine is a genetically heterogeneous (polygenetic)disorder. While there is a strong familial aggregation of migraine (itruns in families) and there is increased concordance for the disease inmono-ygote twins over di-zygote twins, suggesting that it has asignificant genetic component, in part it may be explained byenvironmental determinants. Thus, heritability estimates are calculatedto be between 40 and 60%. The complex genetics of migraine(heterogeneity) may have hampered gene identification. Grouping migrainepatients into one or more subgroups based on identified migrainetriggers may aid in the identification of one or more genetic bases ofand/or in the determination of genetic information for this disease.

Additional applications in determining one or more asthma triggersand/or one or more probable asthma triggers for one or more patientswith asthma, determining one or more drug interactions in one or morepatients with hypertension, and pattern mapping for one or more patientswith diabetes mellitus are described below. The apparatuses andprocesses disclosed may also be used to determine an efficacy of one ormore homeopathic remedies, such as an efficacy of one or more herbs.

Asthma is a chronic inflammatory condition characterized by excessivesensitivity of the lungs to various stimuli. As with migraine patients,many asthma patients are instructed to keep written patient diaries andto attempt to identify patient-specific trigger mechanisms. Asthmatriggers may be specific to an individual, such as at least the firstindividual, or may correspond to one or more groups of individuals.Asthma triggers may be cumulative and/or inter-operative. One or moreasthma triggers may be required to trigger an attack in a respectivepatient.

In some embodiments, entry criteria may include that asthma in therespective patient may be sufficiently well controlled that asthmaattacks are not occurring too often of too infrequently to precludedetermination of triggers. In some embodiments, the subset ofpre-determined questions may be used to collect information for one ormore variables that correspond to one or more potential asthma triggers.Asthma triggers may include respiratory infections (such as viralinfections or colds), allergies (including pollen, mold, animal dander,feather, dust, food, and/or cockroaches), irritating gases (such ascigarette smoke), particles in the air (indoor and/or outdoor airpollutants, including ozone), activities (such as exercise and vigorousexercise), behaviors (such as excitement or stress), and/orenvironmental conditions (such as exposure to cold air or a suddentemperature change). For asthma, the temporal onsets 910 (FIG. 9A) maycorrespond to onset times and/or an onset time intervals for one or moreasthma attacks, which are the events. Statistical analysis may determineone or more statistical relationships between the temporal onsets andpatterns of occurrence of one or more variables and/or one or morecompound variables allowing one or more association variables (asthmatriggers) to be identified. The one or more association variables may beprovided to at least the first individual and/or at least the secondindividual in the form of one or more recommendations and/or one or morereports. The one or more recommendations may include one or more asthmatriggers that at least the first individual may wish to avoid.

In some embodiments, the information collected during thedata-collection time interval may include one or more metricscorresponding to measures of asthma control in at least the firstindividual. The one or more metrics may include day and/or nightsymptoms (such as wheeze and cough), interventions such as a quantifieduse of inhaled bronchodilators (such as a number of treatments withpharmacological agents such as albuterol) and/or oral corticosteroid(including a dosage), daytime activity levels, peak expiratory flow in 1second, and/or forced expiratory volume in 1 second. Day symptoms mayinclude if vigorous activity is okay, if the respective patient can onlyrun briefly, if the respective patient can only walk, and/or if therespective patient must rest at home. Night symptoms may include whetherit was a good night (such as the respective patient slept well but therewas some wheeze or cough, or the respective patient was awake brieflywith wheeze or cough) or a bad night (the respective patient was awakerepeatedly). Wheeze may be described as none, briefly, not troublesome,several times, or continuous. Cough may be described as none, persistentbut not troublesome, interrupted activities once, or interruptedactivities more than once. Peak flow and/or volume may be measured inthe morning (best of three efforts) and/or the evening (best of 3efforts).

Hypertension (high blood pressure) is a multi-factorial disease. As aconsequence, many patients that are treated for hypertension may beprescribed two or more pharmacological agents in an attempt to controlor regulate the disease. Some patients may use dozens of drugsconcurrently. Such pharmacological agents, however, many have sideeffects and there may be interactions between drugs. It may be difficultfor at least the second individual to determine which pharmacologicalagent(s) may be associated with which side effect, and to modify drugchoice and dosage accordingly.

In some embodiments, the subset of pre-determined questions may be usedto collect information corresponding to variables such as whatpharmacological agents were taken and when, what the dosages were (timeand quantity), side effect symptoms, and/or blood pressure data. Someinformation, such as test results corresponding to side effects of oneor more pharmacological agents, may be provided by and/or collected fromat least the second individual. The events and temporal onsets(including onset times and/or onset time intervals) may include improvedregulation of hypertension, poorer regulation of hypertension, areduction in one or more side effects, and/or an increase in a severityof one or more side effects. Statistical analysis, including one or morelook-up tables of possible side effects of (discussed further below)and/or metabolic pathways for one or more pharmacological agents, may beused to determine the one or more statistical relationships and identifythe one or more association variables. One or more side effects may beused as one or more additional inputs 1414 (FIG. 14). One or morerecommendations and/or reports may be provided to at least the firstindividual and/or at least the second individual. These may includesuggestions on one or more association variables that may be avoidedand/or modified (such as for behaviors) to improve control ofhypertension. The one or more recommendations and/or reports may includesuggestions for pharmacological agents and/or dosage (time and/orquantity) to reduce or eliminate one or more side effects and/or toimprove regulation of hypertension.

Pharmacological agents may include diuretics, beta blockers, ACEinhibitors, angiotensin II receptor blockers, calcium channel blockers,alpha blockers, central agonists, peripheral adrenergic inhibitors,and/or blood vessel dilators. Diuretics may decrease potassium levels.As a consequence, “potassium sparing” pharmacological agents such asamiloride, spironolactone or triamterene may be taken concurrently. Sideeffects may include weakness, leg cramps, being tired, gout and/orimpotence. In diabetics, diuretics may change the blood glucose level.This may be addressed by changing the diuretic, diet, activity insulintype, insulin dosage, and/or the use of additional pharmacologicalagents to change insulin sensitivity. Side effects of beta blockers mayinclude insomnia, cold hands and feet, tiredness, depression, a slowheartbeat, symptoms of asthma, and/or impotence. Beta blockers may alsocomplicate the treatment of diabetes mellitus. Side effects of ACEinhibitors may include a skin rash, loss of taste, a chronic dry hackingcough, and/or kidney damage. Side effects of angiotensin II receptorblockers may include dizziness. Side effects of calcium channel blockersmay include heart palpitations, swollen ankles, constipation, headache,and/or dizziness. Side effects of alpha blockers may include a fastheart rate, dizziness, and/or a drop in blood pressure when therespective patient stands up. A combination of alpha and beta blockersmay result in a drop in blood pressure when the respective patientstands up. Side effects of central agonists may include a drop in bloodpressure (including a feeling of weakness and/or fainting) when therespective patient is in an upright position (standing or walking),drowsiness, sluggishness, dryness of the mouth, constipation, fever,anemia, and/or impotence. Side effects of peripheral adrenergicinhibitors may include a stuffy nose, diarrhea, heartburn, nightmares,insomnia, depression, diarrhea, impotence, and/or a drop in bloodpressure when the respective patient stands. Side effects of bloodvessel dilators may include headaches, swelling around the eyes, heartpalpitations, aches or pains in the joints, fluid retention (with amarked weight gain), and/or excessive hair growth.

Diabetes mellitus (including insulin dependence) is a complex diseasethat is often difficult to manage. Diabetes mellitus may be classifiedas one of two types. Type I is thought to be an auto-immune disorderwhere the pancreas is no longer able to produce sufficient insulin. Intype II, cellular membranes may become less sensitive to the effects ofinsulin. In addition, over time the resulting elevated blood glucoselevels may cause the pancreas to produce insufficient insulin. Bloodglucose level is dependent on a large number of time varying,interdependent parameters in several sub-systems in the body. Inaddition, there can be secondary effects on the vascular andneurological systems. Many diabetic patients are instructed to keepwritten patient diary and to attempt to identify reasons why the bloodglucose level is high or low (which is referred to as pattern matching).

In some embodiments, entry criteria for the respective patient, such asat least the first individual, may include classification of the diseaseas early stage, middle stage, or late stage depending on a severity ofsecondary effects of this disease, such as vascular and neurologicaldamage. A level of control of the disease in the respective patient maybe determined to confirm that conditions such as diabetic coma andinsulin shock are either not present or are unlikely to occur. This mayallow reliable information to be collected during the data-collectiontime interval. In some embodiments, the initial survey may includeinformation from at least the first individual and/or at least thesecond individual corresponding to a performance of the liver and/or thekidneys. The performance of the liver and/or the kidneys may be inaccordance with the classification as early stage, middle stage, or latestage. In some embodiments, the initial survey may include a fatpercentage, for example, in accordance with a body mass index. In someembodiments, the initial survey may include measurements of one or moreblood glucose step responses for one or more types of carbohydrates,fats, protein, and/or minerals.

The subset of pre-determined questions may be used to collectinformation corresponding to variables such as diet, activity, use ofone or more pharmacological agents (which may include one or morediabetes medicines and/or one or more insulin types, such as fast actingor slow acting, one or more injection sites, usage times, and/orquantities), a presence of infection (such as a measure of bodytemperature), hydration or dehydration, hormonal changes (such as thoseassociated with pregnancy, puberty, menstruation, and/or menopause),dawn phenomenon, behaviors (such as stress and/or emotion), and/or oneor more daily blood glucose measurements (for example, before meals, 2hours after meals, and/or before bedtime). Diet may include a foodconsumption history, including timing of meals, types of foods, brands,quantities, carbohydrates consumed (which may include amounts andtypes), fats consumed (which may include amounts and types), proteinsconsumed (which may include amounts and types), minerals consumed (whichmay include amounts and types), how full the respective patient was whenhe or she ate, and/or if a respective meal was home made. Activity mayinclude an activity level (such as exercise) and/or an activity history(which may extend over several days).

In some embodiments, the events and temporal onsets (including onsettimes and/or onset time intervals) correspond to deviations of bloodglucose for at least the first individual outside of a regulation band,for example, between 80-120 mg/dl or 80-200 mg/dl. Statistical analysisof one or more temporal onsets and patterns of occurrence of one or morevariables and/or one or more compound variables may be in accordancewith a metabolic model. The metabolic model may include one or morefeedback loops, variable sensitivities, and/or impulse (linear)responses for one or more types of carbohydrates, fats, proteins, and/orminerals. The metabolic model may include a model for glucagon, insulinabsorption, digestion (which may include an absorption rate fordifferent types of carbohydrates, fats, proteins, and/or minerals),storage in the liver (an insulin sensitivity of liver), and/or a renalthreshold for glucose. The metabolic model may also include one or morenon-linear responses, such as for (peripheral) insulin sensitivity,glucose level (which is typically linear within bounds, such as above 70mg/dl and below 300 mg/dl) and behavior/activity (such as activityhistory). The metabolic model may allow extraneous variables to beexcluded in comparisons of different temporal onsets.

One or more statistical relationships may allow one or more associationvariables, such as one or more variables and/or one or more compoundvariables that may be associated with a deviation in blood glucoselevel, to be identified. One or more recommendations and/or one or morereports may be provided to at least the first individual and/or at leastthe second individual in accordance with the one or more identifiedassociation variables. These may include recommendations regarding oneor more association variables that may be avoided and/or modified (suchas for behaviors) to improve control of blood glucose. This may includespecific circumstances (patterns of activity and/or diet) where therecommendations are applicable. The one or more recommendations and/orreports may include pharmacological agents and/or dosage (time and/orquantity) to improve control of blood glucose. Adjustments to insulin,for example, may include a mean daily dosage and/or day-to-dayvariations in dosage about the mean, such as a change in one or morequantities of insulin and/or one or more fewer or additional injections.Patterns of activity may include a sequence of activities over a day,several days, and/or a week. For example, the sequence of activities mayinclude an exercise history.

The system and analysis techniques described may also be used forweb-related applications, e.g., to determine the relevant variablesand/or compound variables that internet advertisement may exhibit inorder for users to click on them or pursue them as a function of time.The advertisement variables may include word content, graphicalproperties, colors, animation and/or appearance. In another embodiment,the system and analysis techniques describe may be used innetwork-related applications to determine the variables and/or compoundvariables that may lead to the network going down or experience a fatalcrash as a function of time. The variables may include an amount and acontent of network traffic (registered at nodes, routers, servers,connections, etc.), caching, package requests posted by users, packagerequests received by servers, routing decisions, a number or percentageof packages that arrive corrupted and/or network delays.

While embodiments of apparatuses and related methods for determining oneor more association variables have been described, the apparatuses andrelated methods may be applied generally to determine statisticalrelationships between one or more temporal onsets corresponding to oneor more events and patterns of occurrence of one or more variablesand/or one or more compound variables in a wide variety of statisticalleaning problems, in medicine, psychology, statistics, engineering,applied mathematics and operations research.

The foregoing description, for purposes of explanation, used specificnomenclature to provide a thorough understanding of the invention.However, it will be apparent to one skilled in the art that the specificdetails are not required in order to practice the invention. Theforegoing descriptions of specific embodiments of the present inventionare presented for purposes of illustration and description. They are notintended to be exhaustive or to limit the invention to the precise formsdisclosed. Obviously many modifications and variations are possible inview of the above teachings. The embodiments were chosen and describedin order to best explain the principles of the invention and itspractical applications, the thereby enable others skilled in the art tobest utilize the invention and various embodiments with variousmodifications as are suited to the particular use contemplated. It isintended that the scope of the invention be defined by the followingclaims and their equivalents.

1. A computer-implemented method for determining one or more migrainetriggers associated with one or more migraines, comprising: determining,using the computer, a statistical relationship between one or moretemporal onsets associated with one or more events and a pattern ofoccurrence of a compound variable, wherein the one or more eventsinclude the one or more migraines experienced by at least oneindividual, wherein the compound variable corresponds at least to apattern of occurrence of a first variable and a pattern of occurrence ofa second variable, and wherein the statistical relationship includescontributions from presence and absence information in the pattern ofoccurrence of the compound variable; and identifying, via a processor,the first variable and the second variable as the migraine triggersbased on the statistical relationship.
 2. The method of claim 1, whereinthe pattern of occurrence of the first variable is during a first set oftime intervals and the pattern of occurrence of a second variable isduring a second set of time intervals, and wherein a respective timeinterval in a respective set of time intervals, which can include thefirst set of time intervals or the second set of time intervals,precedes a respective temporal onset in the one or more temporal onsets.3. The method of claim 2, wherein time intervals in at least one of thefirst set of time intervals and the second set of time intervals areoffset in time from the one or more temporal onsets.
 4. The method ofclaim 2, wherein time intervals in the first set of time intervals aredifferent than time intervals in the second set of time intervals. 5.The method of claim 1, further comprising receiving informationincluding the one or more temporal onsets associated with the one ormore events and the pattern of occurrence of the compound variable. 6.The method of claim 1, wherein the determining uses a non-parametricstatistical analysis technique including a chi-square analysistechnique, a log-likelihood ratio analysis technique, or a Fisher'sexact probability analysis technique.
 7. The method of claim 1, whereinthe determining uses a supervised learning technique including a supportvector machines (SVM) analysis technique, a shrinkage and regressiontechnique, or a classification and regression tree (CART) analysistechnique.
 8. The method of claim 1, wherein the pattern of occurrenceof the first variable and the pattern of occurrence of the secondvariable comprise categorical data, and wherein a respective entry inthe pattern of occurrence of the compound variable is determined byperforming a logical operation on corresponding entries in the patternof occurrence of the first variable and the pattern of occurrence of thesecond variable.
 9. The method of claim 8, wherein the logical operationis a Boolean operation selected from a group consisting of AND, OR, NOTand XOR.
 10. The method of claim 1, further comprising determiningstatistical relationships for a plurality of compound variables, whereina respective compound variable in the plurality of compound variablescorresponds to patterns of occurrence of at least two variables in a setof variables, one of at least the two variables occurring during one setof time intervals and another of at least the two variables occurringduring another set of time intervals, and wherein a respective timeinterval in a respective set of time intervals, which can include theset of time intervals or the other set of time intervals, precedes arespective temporal onset in the one or more temporal onsets.
 11. Themethod of claim 10, further comprising determining a ranking of thevariables in the set of variables based on aggregate properties of thevariables in at least a subset of the plurality of the compoundvariables, wherein the statistical relationships associated with atleast the subset of the plurality of the compound variables exceed astatistical confidence threshold.
 12. The method of claim 11, whereinthe aggregate properties include the number of occurrences of thevariables in at least the subset of the plurality of the compoundvariables.
 13. The method of claim 1, wherein the pattern of occurrenceof the first variable and the pattern of occurrence of the secondvariable include presence and absence information.
 14. The method ofclaim 1, further comprising identifying one or more additional migrainetriggers of at least the one individual based on the presence of theidentified migraine triggers in one or more groups of migraine triggersthat were previously determined for one or more other individuals. 15.The method of claim 1, wherein one or more of the migraine triggers atleast in part induce a migraine in at least the one individual if atleast the one individual is exposed to one or more of the migrainetriggers.
 16. A computer-implemented method for determining one or moremigraine triggers associated with one or more migraines, comprising:receiving a first data stream including one or more temporal onsetsassociated with one or more events, a pattern of occurrence of a firstvariable and a pattern of occurrence of a second variable, wherein theone or more events include the one or more migraines experienced by atleast one individual; identifying, using the computer, the one or moremigraine triggers associated with the one or more migraines based on astatistical relationship between the one or more temporal onsets and apattern of occurrence of a compound variable, the compound variablecorresponding at least to the pattern of occurrence of the firstvariable and the pattern of occurrence of the second variable, andwherein the statistical relationship includes contributions frompresence and absence information in the pattern of occurrence of thecompound variable; and transmitting a second data stream includinginformation that identifies the first variable and the second variablesas the migraine triggers.
 17. The method of claim 16, wherein the one ormore migraine triggers are identified based on statistical relationshipsbetween the one or more temporal onsets and patterns of occurrence of aplurality of compound variables, wherein a respective compound variablein the plurality of compound variables corresponds to patterns ofoccurrence of at least two variables in a set of variables, one of atleast the two variables occurring during one set of time intervals andanother of at least the two variables occurring during another set oftime intervals, and wherein a respective time interval in a respectiveset of time intervals, which can include the set of time intervals orthe other set of time intervals, precedes a respective temporal onset inthe one or more temporal onsets.
 18. The method of claim 17, wherein theone or more migraine triggers are identified based on a ranking of thevariables in the set of variables based on aggregate properties of thevariables in at least a subset of the plurality of the compoundvariables, and wherein the statistical relationships of at least thesubset of the plurality of the compound variables exceed a statisticalconfidence threshold.
 19. The method of claim 16, further comprisingidentifying one or more additional migraine triggers of at least the oneindividual based on the presence of the identified migraine triggers inone or more groups of migraine triggers that were previously determinedfor one or more other individuals.
 20. The method of claim 16, whereinone or more of the migraine triggers at least in part induce a migrainein at least the one individual if at least the one individual is exposedto one or more of the migraine triggers.